130 Matching Annotations
1. Last 7 days
2. theconversation.com theconversation.com
1. 60% of Africa’s food is based on wheat, rice and maize

stats - 60% of Africa's food is composed of: - wheat - rice - maize

for - stats - Africa - main crops - Africa - main crops

#### URL

3. Feb 2024
4. www.ribaj.com www.ribaj.com
1. The Inventory of Embodied Carbon and Energy 2019 says ‘general stone’

for - stats - carbon footprint of stone, steel, concrete

stats - carbon footprint - stone, steel , concrete - ( see below)

• The Inventory of Embodied Carbon and Energy 2019 says carbon footprint of the following building materials are:
• ‘General stone’ - 0.079kg carbon per kg .
• Concrete - 0.15kg carbon per kg and
• Steel - 2.8kg carbon per kg.
2. Is there enough stone?

for - stone availability - stats - stone availability

stone stats - rough calculation below

• Question: Is there enough stone?
• According to the Global Cement and Concrete Association,
• annual worldwide concrete production is roughly 1.6 km3.
• Due to its higher strength its equivalent in stone would be about one quarter of that volume.
• To put this into context,
• the volume of a small, Ben Nevis-ish mountain is about 30km3;
• all the world’s buildings* would only make a 56km3 or two Nevis,
• the Earth’s crust (rock) has a volume of 10 billion km3.
• Assumptions for above calculations:
• 7bn people living in threes in
• 120m2 live work units made of
• 200mm slabs.

#### URL

5. docdrop.org docdrop.org
1. one of the core ways that we're weird is that we think we have a self

for - definition - Weird - stats - Weird countries - greatest sense of self - inspiration - introduce - Sarah Stein Lubrano - Rachell - Indyweb - Indranet

definition - Weird - Western Educated Industrialized Rich Democratic

inspiration - introduce Rachel and Sarah to Indyweb / Indranet - As soon as I heard Rachel and Sarah talk about the prominent and unique WEIRD feature of sense of self, - I immediately thought that we must introduce them to our work on the Indyweb / |ndranet as our system is designed based on the epistemology that - we are not a thing - we are a process - we are evolution in realtime action - the very use of the Indyweb / Indranet reinforces the reality that we are a process and not a fixed entity - so deconstructs the social construct of the self

#### URL

6. Jan 2024
7. time.com time.com
1. The current silver economy stands at

for - silver economy - stats - silver economy

stats - silver economy - 2024 - 7 trillion yuan ($982 billion USD) - 6 % GDP - 2035 - 30 trillion yuan ($4.2 trillion USD) - 10% GDP

question - silver economy - climate change impacts? transition impacts?

#### URL

8. circle.tufts.edu circle.tufts.edu
1. In the next presidential election, 40.8 million members of Gen Z (ages 18-27 in 2024) will be eligible to vote,

for - Gen Z influence on 2024 US election - Trump 2024 win - an existential threat to humanity - stats - Gen Z - 2024 U.S. election

comment - Gen Z can play a role in determining the future of human civilization. How? Their vote in the upcoming 2024 U.S. election. If Donald Trump wins, it can pose an existential threat to human civilization - https://hyp.is/mwqwpsA-Ee6bAd9C2MLeKg/www.msnbc.com/opinion/msnbc-opinion/trump-2024-presidency-climate-change-rcna131928

stats - Gen Z - 2024 U.S. election

• In the next presidential election, 40.8 million members of Gen Z (ages 18-27 in 2024) will be eligible to vote,
• including 8.3 million newly eligible youth (ages 18-19 in 2024)
• who will have aged into the electorate since the 2022 midterm election.
• These young people have tremendous potential to
• influence elections and to
• spur action on issues they care about
• if they are adequately reached and supported by parties, campaigns, and organizations.

#### URL

9. docdrop.org docdrop.org
1. nearly one third of the world's population is still lives in an earthen building

for - stats - % of people inhabiting earthen buildings

#### URL

10. religionnews.com religionnews.com
1. only 11% say they are involved in a religious community.

for - stats - spiritual but not religious

stats - spiritual but not religious - Pew research study shows 22% of Americans now identify as spiritual but not religious - Only 11% say that are involved in a religious community

#### URL

11. theconversation.com theconversation.com
1. African countries have become reliant on a few food items.

for - stats - Africa - food insecurity - adjancency - food colonialism - food insecurity - food dependency

stats - food insecurity - 20 plant species make up 90% of food consumed in Africa - 3 crops introduced by the Green Revolution make up 60% of all calories consumed - wheat - maize - rice

• African countries have become reliant on a few food items.

• Just 20 plant species now provide 90% of our food, with three

• wheat,
• maize and
• rice

accounting for 60% of all calories consumed on the continent and globally.

• This deprives the continent of diverse food sources,

at the very time when research has found

massive food and nutrition insecurity in Africa.

• By 2020, about 20% of the continent’s population (281.6 million) faced hunger.
• This figure is likely to have increased,
• given the impacts of successive droughts, floods and COVID-19.

- 30,000 edible plant species, and
- 7,000 were traditionally cultivated or foraged for food.


The continent is a treasure trove of agrobiodiversity (a diversity of types of crops and animals) and

• its countries could easily feed themselves.

#### URL

12. www.sciencedirect.com www.sciencedirect.com
1. for - climate crisis - food production impacts - stats - high emissions scenario -food production

stats - high emissions scenario - over 30% of food crop production and animal production impacted - mostly around equator

#### URL

13. www.repubblica.it www.repubblica.it
1. All stakeholders in the world must now act according to the agreed Cop28 output, and deliver on the CopP28 Global Stocktake Agreement, which means rapidly transitioning away from oil, coal and gas, aiming at more than 40% reductions by 2030
• for: climate mitigation, stats - 40% reduction by 2030, quote - Johan Rockstrom, quote - fossil fuel phase out

• quote: Johan Rockstrom

• All stakeholders in the world must now act according to the agreed Cop28 output, and deliver on the CopP28 Global Stocktake Agreement,
• which means rapidly transitioning away from oil, coal and gas, aiming at more than 40% reductions by 2030
• Date: Dec 31, 2023

#### URL

14. Dec 2023
15. docdrop.org docdrop.org
1. the actual capture was about 7 million tons of carbon dioxide that's under 2 hours of global C2 emissions now that's after 20 00:02:57 years of the gates of this world being p pushing that technology so we captured was it .198 I think per of our CO2 emissions in 2021 and the global CCS Institute said 00:03:14 that if all of their plans come to fruition then by about 2030 we might capture about 45 to 49 million tons so about 1% of all of our carbon dioxide
• for: stats - CCS, Kevin Anderson - CCS stats

• stats

• actually, IEA shows that we are currently capturing 45 million tons currently (Dec 2023)
• 45 million tons captured in 2021 (IEA CCS 2021)
• out of 36.3 billion tonnes emitted (IEA, 2021)
• 0.124% captured in 2021 and
• 3x current rate at 125 Mt/yr by 2030
• far from enough
• reference

#### URL

16. www.iea.org www.iea.org
1. 2030, capturing around 125 Mt CO2 per year
• for: stats - CCS, stats - CCUS

• stats

• 125 Mt / year by 2030
2. There are now around 40 commercial capture facilities in operation globally, with a total annual capture capacity of more than 45 Mt CO2.
• for: stats - CCS 2021, CCUS - 2021

• stats

• CCS / CCUS in 2021: 45 Mt CO2

#### URL

17. docdrop.org docdrop.org
1. one thing we have noticed with these stresses is that uh from most of them we see these three 00:11:11 phenomena the stresses are amplifying accelerating and synchronizing uh simultaneously
• for: pernicious cascades - qualities, stats - pernicious cascade, synchronized crisis

• stats - pernicious cascades - climate change

• amplifying
• global mean temp
• in 2000: 0.72 Deg C warmer than pre-industrial (1850)
• in 2020 1.2 Deg C warmer than pre-industrial
• accelerating
• 1970 to 2010 - 0.18 Deg C / decade
• 2010 to 2040 - 0.27 Deg C / decade
• syncrhonizing
• many systems are becoming unstable:
• higher inflation
• higher levels of precarity
• higher levels of inequality
• destabilizing climate / increase in extreme weather events
• geopolitical conflicts
• political polarization
• misinformation
• major migration
• Cascade institute does not view these as random events - not a set of coincidence or "perfect storm"
• instead, invisible synchronization and causality between events
• our research reveals the invisible connections
2. they're probably about 15 or 20 major long-term stresses that you can identify that are affecting 00:09:43 Humanities outcomes for Better or For Worse and Trigger events which which are much less predictable
• for: stats - major stressors of the polycrisis, trigger events

• stats: major stressors of the polycrisis

• profile of a crisis
• a crisis occurs when
• a major stressor occurs
• there are between 15 and 20 of them
• and combines with much less predictable "trigger" events
• also called stochastic or random events

#### URL

18. www.fastcompany.com www.fastcompany.com
1. if you bank with one of the largest 11 banks in the U.S., the report suggests using the rough estimate of 0.24 metric tons of CO2 for every $1,000 you have in the bank. Between 20% and 30% of your money is likely used in fossil fuel projects or other carbon-intensive sectors like mining. • for: stats - bank emissions • stats: bank emissions • if your bank is equivalent to the largest 11 US banks, the Project Drawdown report estimates • 0.24 metric tons of CO2 for every$1,000 USD saved in the bank
• between 20 to 30 percent of your money is likely used to finance fossil fuel projects or other carbon intensive sectors like mining.

#### URL

19. thetyee.ca thetyee.ca
1. Cutting emissions back to bring global temperatures down to 1.5 C or 2 C would be the equivalent of shutting down China, the United States, India, Japan and Russia.
• for: stats - staying under 1.5 Deg C

• stats: staying under 1.5 Deg C

• is equivalent to shutting down the economies of China, the US, India, Japan and Russia

#### URL

20. Nov 2023
21. www.iea.org www.iea.org
1. Economies that are heavily reliant on oil and gas revenues face some stark choices and pressures in energy transitions.
• for: stats - oil and gas - steep drop in revenues of fossil fuel producer economies

• stats: oil and gas - steep drop in revenues of fossil fuel reliant economies

• per capita net income from oil and natural gas among producer economies will be 60% lower in 2030 in a 1.5 °C scenario.relative to revenues between 2010 and 2022.
• question

• many producer economies are not diversifying into clean energy fast enough to compensate for these steep revenue drops
2. For producers that choose to diversify and are looking to align with the aims of the Paris Agreement, our bottom-up analysis of cash flows in a 1.5 °C scenario suggests that a reasonable ambition is for 50% of capital expenditures to go towards clean energy projects by 2030, on top of the investment needed to reduce scope 1 and 2 emissions.
• for: stats - oil and gas industry - required investments in clean energy

• stats: oil and gas industry - required investments in clean energy

• 50 % of capital expenditure by 2030 and reduction in scope 1 and 2 emissions
• comment

• Wow, is it really possible for the industry to spend 50 % of their budget on clean energy in 7 years? This would be unprecedented, given that greenwashing is all we've ever seen in the past.
3. Some 30% of the energy consumed in a net zero energy system in 2050 comes from low-emissions fuels and technologies that could benefit from the skills and resources of the oil and gas industry.
• for: stats - oil and gas industry - repurposing for clean energy

• stats: oil and gas industry - repurposing for clean energy

• only 30 % of the energy consumed in a clean energy future within 1.5 Deg C comes from low emission fuels and technologies that benefit from oil and gas industry resources
• this leaves a huge deficit of 70 %.
• question

• How will the transition account for these human and technological resources?
4. Many producers say they will be the ones to keep producing throughout transitions and beyond. They cannot all be right.
• for: stats - oil and gas industry - fight for survival

• stats: oil and gas industry - fight for survival

• competing oil producers will have to reach an agreement on who has the right to produce the remaining carbon budget
• 24 million barrels a day are still produced in a 1.5 Deg C scenario but are largely uncombusted
• 75 % of that will be used in petrochemical and other industry
• 920 billion cubic meters of natural gas
• 50% of this for hydrogen production
5. In a scenario that hits global net zero emissions by 2050, declines in demand are sufficiently steep that no new long lead-time conventional oil and gas projects are required. Some existing production would even need to be shut in. In 2040, more than 7 million barrels per day of oil production is pushed out of operation before the end of its technical lifetime in a 1.5 °C scenario.
• for: stats - oil and gas industry - steep drop in production

• stats - oil and gas industry - steep drop in production

• no new fields can be developed to meet a 1.5 Deg C scenario
• any new developments face the certain risk of being a stranded asset
• by 2040, 7 million less barrels of oil are produced each day to meet a 1.5 Deg C scenario
6. The production, transport and processing of oil and gas results in just under 15% of global energy-related greenhouse gas emissions. This is a huge amount, equivalent to all energy-related greenhouse gas emissions from the United States.
• for: stats - oil and gas industry operational emissions

• stats: oil and gas industry - operational emissions

• 15% of all global emissions are from the production, transport and processing of fossil fuels
7. Oil and gas producers account for only 1% of total clean energy investment globally.
• for: stats - oil and gas industry - clean energy investments

• comment

• Inclusive transformation
• Clearly, transforming the dirty fossil fuel industry into clean energy industry requires migrating as much of those 12 million dirty energy jobs as possible. We can't alienate the fossil fuel industry.
• the barometer to measure this paradigm shift in fossil fuel industry narrative is their investment into clean energy. Over the years, majors have acted like politicians, promising significant clean energy investment, then backsliding. There is no more time for that.
8. This new IEA report explores what oil and gas companies can do to accelerate net zero transitions and what this might mean for an industry which currently provides more than half of global energy supply and employs nearly 12 million workers worldwide.
• for: stats - oil and gas industry - profit split, stats - oil and gas industry - reserves split

• stats: oil and gas industry profit split

• 50 % to governments
• 40 % to investments
• 10% to shareholders and debt
• stats: oil and gas reserve splits

• majors: 13 % production, 13 % reserves
• National Oil Companies: 50% production, 60 % reserves
9. Oil and gas projects currently produce slightly higher returns on investment, but those returns are less stable.
• stats - oil and gas vs clean energy returns

• stats: oil and gas vs clean energy returns between 2010 and 2022

• 6 to 9 % for oil and gas
• 6 % for clean energy
10. If all national energy and climate goals are reached, this value is lower by 25%, and by 60% if the world gets on track to limit global warming to 1.5 °C.
• for: stats - fossil fuel industry - valuation in a 1.5 Deg C world

• stats: fossil fuel industry - valuation in a 1.5 Deg C world

• current 2023 valuation: 6 trillion USD
• current NDCs met (short of a 1.5 Deg C world): 4.5 trillion USD
• 1.5 Deg C world: 2.4 trillion USD
11. To align with a 1.5 °C scenario, these emissions need to be cut by more than 60% by 2030 from today’s levels and the emissions intensity of global oil and gas operations must near zero by the early 2040s.
12. The production, transport and processing of oil and gas results in just under 15% of global energy-related greenhouse gas emissions.

for: stats - oil and gas industry, stats - fossil fuel industry

• stats: oil and gas industry
• stats: fossil fuel industry
• The fossil fuel industry's production, transport and processing operations accounts for 15% of global energy-related greenhouse gas emissions.
13. Oil and gas producers account for only 1% of total clean energy investment globally.
• for: stats - oil and gas industry, stats - fossil fuel industry

• stats - oil and gas industry

• stats - fossil fuel industry
• Oil and gas producers account for approximately 1% of total clean energy investment
• 60 % of that comes from 4 companies
14. industry which currently provides more than half of global energy supply and employs nearly 12 million workers worldwide.
• for: stats - oil and gas industry, stats - fossil fuel industry

• stats - oil and gas industry

• stats - fossil fuel industry
• supplies approximately 50% of all total global energy
• employs 12 million people directly
• Since 2018, annual revenues average 13 trillion USD
• revenue split
• 50 % to governments
• 40% to investment
• 10% to shareholders and debt
• Major oil companies account for 13 % of all reserves
• National Oil Companies (NOC) account for
• over 50% of all production
• close to 60% of all reserves
15. if governments deliver in full on their national energy and climate pledges, then oil and gas demand would be 45% below today's level by 2050 and the temperature rise could be limited to 1.7 °C. If governments successfully pursue a 1.5 °C trajectory, and emissions from the global energy sector reach net zero by mid-century, oil and gas use would fall by 75% to 2050.
• for: Nationally Determined Contributions insufficient to meet 1.5 Deg C, NDC insufficient to meet 1.5 Deg C

• stats: climate change - NDC

• current NDCs
• 45% reduction in fossil fuel usage by 2050
• NDCs to meet 1.5 Deg C
• 75% reduction in fossil fuel usage by 2050

#### URL

22. docdrop.org docdrop.org
1. when we're looking here at sleep apnea we're looking at these bars here and you can see that people with 00:06:21 sleep apnea the most likely time for them to die is between midnight and six o'clock in the morning and you can imagine why that would be
• for: stats - sleep apnea - most likely time to die

• stats: sleep apnea

• most likely to die between midnight and 6am
2. sometimes this 00:04:37 can happen up to a hundred times in an hour that means at least once a minute or more maybe even twice a minute that this is happening you can expect that people are not going to get very good sleep with this
• for: stats - sleep apnea cycle

• stats: sleep apnea cycle

• can happen up to 100 times an hour!

#### URL

23. Oct 2023
24. abcnews.go.com abcnews.go.com
1. the survey also found that 34% of respondents reported feeling loneliness and 44% reported feeling a sense of not mattering to others.

I feel like college (on paper) do so much to help their students adjust to their new lives and the new world that they are exposed to on campus. What can they change to make this better?

#### URL

25. Sep 2023
26. docdrop.org docdrop.org
1. people generally don't recognize is that forest across the planet has responded in a tremendously helpful way 00:16:29 by absorbing roughly 25% of carbon dioxide from our fossil fuel burning. And we generally talk about this as a positive. "Isn't that fantastic!" But, in reality, it's a stress response.
• for: carbon sinks, carbon sinks - oceans, carbon sinks - forests, stats, stats, forest carbon sink, stats - ocean carbon sink, question, question - when do carbon sinks turn into carbon sources?
• stats

• forests are absorbing 25% of carbon dioxide emissions
• oceans are absorbing 50% of carbon dioxide emissions
• these are stressing these carbon sinks
• question

• how much longer can they absorb without unintended consequences playing out?

#### URL

27. www.science.org www.science.org
1. Based on an analysis of suitable habitats, the researchers estimate the ants could invade 7% of the European continent.
• for: stats, stats - invasive species - red fire ants,
• stats:
• prefers farms and cities
• could infest 50% of urban areas
• could inhabit up to 7% of EU continent
• infestation vectors
• walk
• fly
• carried by wind
• shipping
• Sicily, the site of discovery, ships a variety of plants across the Mediterranean
• Southern Spain offers ideal climate for red fire ants
• Climate change could increase its range by 25% by 2050.

#### URL

28. macleans.ca macleans.ca
1. Hotter, harder-to-contain fires will burn indefinitely
• stats: 2017
• pyrocumulonimbus cloud rose 13 km into the stratosphere, a world record
• 12,000 square kilometers burned
• stats: 2023
• to the date of this article (Sept 1, 2023), 100,000 square kilometers burned
2. Cities across the country will begin to reach “climate departure”: a symbolic rubicon, after which a climate falls completely outside historical norms.
• for: climate departure, Camilo Mora, stats, stats - climate departure - canada, climate departure - montreal, climate departure - vancouver, climate departure - toronto
• paraphrase
• Cities across the country will begin to reach “climate departure”: a symbolic rubicon, after which a climate falls completely outside historical norms.
• Even the coldest year, going forward, will be hotter than the hottest in the past.
• The concept was defined in 2013 by researchers at the University of Hawai’i, who crunched computer models of 39 different planetary futures to arrive at their predictions.
• In a scenario consistent with roughly two degrees warming by mid-century,
• stats: start - Montreal is estimated to reach its departure point in 2072, - Toronto in 2074 and - Vancouver in 2083.
• stats: end
• comment
• the article doesn't mention two important points
• a number of places are expected to reach climate departure in the 2020's, such as
• Manokwari, Indonesia in 2020
• Lagos and Jakarta in 2029
• Even if we decarbonize at the most aggresive RCP pathway, it would not prevent climate departure, but only delay it by a few decades
• The implications are profound. It means that the living organisms on most places on the planet will be on a path to extinction or migration. The entire biosphere will be in migration and this also has profound implications on human social and economic systems. Species whose livelihood billions of people depend on will be migrating to other parts of the environment, potentially devastating large swathes of local economies the world over.
• Reference:
• climate departure global map (2013)
• Annotation of Dr. Camilo Mora's 2013 paper on climate departure:

#### URL

29. Aug 2023
30. iopscience.iop.org iopscience.iop.org
1. the systemwide optimum population cohort for the climate action interventions is a community (P4) of 10 000 persons
• for: cross-scale translation of earth system boundaries, downscaled planetary boundaries, leverage point

• stats

• 10000 to 1 million is optimum size
• question: investigate rationale

#### URL

31. www.mdpi.com www.mdpi.com
1. Demographic and Socioeconomic Correlates of Disproportionate Beef Consumption among US Adults in an Age of Global Warming
• for: climate change impacts - dietary, climate change impacts - meat eating, carbon footprint - meat, leverage point - meat eating
• title: Demographic and Socioeconomic Correlates of Disproportionate Beef Consumption among US Adults in an Age of Global Warming
• author: Donald Rose
• date: Aug. 30, 2023

• stats

• study based on NHANES study of 10, 248 U.S. adults between 2015 and 2018 indicated that 12% accounted for all beef consumed

#### URL

32. www.futurity.org www.futurity.org

#### URL

33. www.vancouverisawesome.com www.vancouverisawesome.com
1. None of the 28 streams Cunningham and his colleagues studied hit summertime highs warmer than 25.9 C, the point where warming water can become lethal. But in four rivers, temperatures climbed past 20.3 C, the threshold where some have found juvenile coho stop growing.
• for: climate change - impacts, extinction, biodiversity loss, fish kill, salmon dieoff, stats, stats - salmon, logging, human activity

• paraphrase

• stats

• None of the 28 streams Cunningham and his colleagues studied hit summertime highs warmer than 25.9 C,
• the point where warming water can become lethal.
• But in four rivers, temperatures climbed past 20.3 C,
• the threshold where some have found juvenile coho stop growing.
• In some watersheds, deforestation rates climbed to 59 per cent.
• comment

• deforestation may be a contributing factor but there are also other variables like changes in glacial melt water
2. One study found once temperatures climb past 20.3 C, salmon stop growing because they can't get enough food to satisfy their metabolism.

-for: salmon survival temperature, stats, stats - salmon, salmon dieoff, climate change - impacts, fish kill - paraphrase -stats - One study found once temperatures climb past 20.3 C, - salmon stop growing because they can't get enough food to satisfy their metabolism.

#### URL

34. hackernoon.com hackernoon.com
1. In our early experiments, reported by The Washington Post in March 2013, we discovered that Google’s search engine had the power to shift the percentage of undecided voters supporting a political candidate by a substantial margin without anyone knowing.
• for: search engine manipulation effect, SEME, voting, voting - bias, voting - manipulation, voting - search engine bias, democracy - search engine bias, quote, quote - Robert Epstein, quote - search engine bias, stats, stats - tilting elections
• paraphrase
• quote
• In our early experiments, reported by The Washington Post in March 2013,
• we discovered that Google’s search engine had the power to shift the percentage of undecided voters supporting a political candidate by a substantial margin without anyone knowing.
• 2015 PNAS research on SEME
• http://www.pnas.org/content/112/33/E4512.full.pdf?with-ds=yes&ref=hackernoon.com
• stats begin
• search results favoring one candidate
• could easily shift the opinions and voting preferences of real voters in real elections by up to 80 percent in some demographic groups
• with virtually no one knowing they had been manipulated.
• stats end
• Worse still, the few people who had noticed that we were showing them biased search results
• generally shifted even farther in the direction of the bias,
• so being able to spot favoritism in search results is no protection against it.
• stats begin
• with or without any deliberate planning by Google employees
• was currently determining the outcomes of upwards of 25 percent of the world’s national elections.
• This is because Google’s search engine lacks an equal-time rule,
• so it virtually always favors one candidate over another, and that in turn shifts the preferences of undecided voters.
• Because many elections are very close, shifting the preferences of undecided voters can easily tip the outcome.
• stats end

#### URL

35. docdrop.org docdrop.org
1. One hundred trillion cells, one and 14 zeros, that's the approximate number of microorganisms in your body, ten times greater than the number of your own cells. Your microbial baggage occupies almost 2% of your body weight, that's about one and a half kilograms, approximately the weight of your liver. Or your brain.
• for: stats, stats - microbiome, human microbiome, stats - human microbiome
• stats
• paraphrase
• One hundred trillion cells is the approximate number of microorganisms in your body,
• that's ten times greater than the number of your own cells.
• The micrbiome is about 2% of our body weight
• That's about one and a half kilograms

#### URL

36. aeon.co aeon.co
1. attrition rates for intentional communities are not all that different from many other types of human endeavour.
• for: stats, intentional community, intentional communities, - stats - intentional communities
• intentional communities fail at a rate slightly higher than most startups
• startup failure rate is around 90%
• longevity of Fortune 500 companies listed in 1955 to 2017
• failure rate of 88%
• S&P companies average lifespan: 15 years
2. Generally, intentional communities fail at a rate slightly higher than that of most start-ups. Only a handful of communities founded in the US during the 19th century’s ‘golden age of communities’ lasted beyond a century; most folded in a matter of months. This golden age birthed more than 100 experimental communities, with more than 100,000 members in total who, according to the historian Mark Holloway in Heavens on Earth (1951)
• for: stats, intentional community, intentional communities, stats - intentional communities
• intentional communities fail at a rate slightly higher than most startups

#### URL

37. docdrop.org docdrop.org
1. what I'm advocating here isn't radical redistribution it's merely more 00:13:08 redistribution in a and structurally dependable manner that is fair that is inclusive and that allows for the poor and improvised Nations to be granted excess not just a vital strategic resources that are very much needed in 00:13:21 maintaining the quality of life at own citizens but also more importantly the ropes to climb the ladder
• for: W2W, TPF, stats, inequality, wealth redistribution, wealth tax, quote, quote - wealth tax, quote - inequality, stats, stats - inequality, stats - wealth tax
• quote
• stats
• An annual wealth tax of just 5% on multi-millionaires and billionaires
• could raise US $1.7 trillion a year • enough to lift 2 billion people out of poverty • author Institute for Policy (2023) • comment • that breaks down to approximately$US 1,000 per person for 2 billion people from the 1% elites
• this is pretty reasonable
• W2W can begin with this simple VOLUNTARY ASK
• if the multi-millionaires and billionaires do just this consistently, then it is so little from their coffers and they could avoid a wealth tax by simply stepping up voluntarily
• Could W2W motivate them to?

#### URL

38. areomagazine.com areomagazine.com
1. Estimates indicate that nearly 20–30% of our male ancestors died in intergroup conflicts.
• for: stats, quote, stats - homophobia - war, quote - homophobia - war, evolution - homophobia, homophobia - war
• quote
• stats
• estimates indicate that nearly 20-30% of our male ancestors died in intergroup conflicts
• comment
• wow!

#### URL

39. areomagazine.com areomagazine.com
1. 478 intentional communities since the 1820s have now shrunk to 112 worldwide in the last 30 years)
• for: intentional community, intentional communities, intentional communities - failure, stats, stats - intentional communities
• stats
• of 478 intentional communities since the 1820s,
• 112 exist worldwide in the last 30 years (1988 - 2018
• for: intentional community, intentional communities, intentional communities - failure, stats, stats - intentional communities
• stats
• of 478 intentional communities since the 1820s,
• 112 exist worldwide in the last 30 years (1988 - 2018

#### URL

40. bonpote.com bonpote.com
1. The Shift Project has estimated that if only 3% of festival-goers attending the Vieilles Charrues Festival come by plane, they account for more than 60% of carbon emissions linked to public transport!
• for carbon inequality, carbon emissions - air travel, carbon emissions - concerts, stats - air travel - concerts
• paraphrase
• stats
• The Shift Project has estimated that
• if only 3% of festival-goers attending the Vieilles Charrues Festival come by plane, they account for more than 60% of carbon emissions linked to public transport!
• Tomorrowland concert - close to 25,000 festival-goers fly in via "party flights"
• North America Burning Man - 20% of festival goers fly in
• In general, the largest footprint for famous cultural events is air travel

#### URL

41. Mar 2023
42. www.sciencedirect.com www.sciencedirect.com
1. 1% of the world's population is responsible for an estimated 50% of emissions from commercial air transport, most of this associated with premium class air travel of affluent frequent fliers
• Quote
• carbon inequality stat
• 1% of the world's population is responsible for 50% of emissions from commercial air transport
2. 5245 superyachts with lengths of 30–180 m in 2021, a five-fold increase from 1090 yachts in 1990

yacht stats - 2021: 5245 superyachts of lengths 30-180m - 1990: 1090 superyachts of lengths 30-180m - stats - yachts - quote - yachts

3. the top 1% now being responsible for 17% of total emissions, the top 10% for 48%, and the bottom half of the world population for only 12% (in 2019).

Quotable carbon inequality stats: - the top 1% responsible for 17% of total emissions, - the top 10% for 48%, - the bottom 50% for12% - stats carbon inequality - quote carbon inequality - 2019

// A key question is also this: - what are individuals using those carbon emissions for? - is it being used just for luxury consumption - or is it being used to develop and actionize scalable low carbon strategies? - if it is the later, it could be seen as a de-carbon investment

4. close to two thirds of the overall carbon footprint of those billionaires owning yachts is caused by yacht-ownership. This implies a contribution to climate change that is up to 6500 times greater for these individuals than the global average of 4.5 t CO2 per capita and year, or up to 300,000 times greater than the contribution of the poorest, at 0.1 t CO2 per person and year

Yacht stats: - close to two thirds of the overall carbon footprint of those billionaires owning yachts is caused by yacht-ownership. - Carbon footprint is - 6500 times greater than the global average of 4.5 t CO2 per person per year, - 300,000 times greater than the poorest, at 0.1 t CO2 per person and year - stats - carbon inequality - quote - carbon inequality

5. the top 0.01% emitting in excess of 2300 t CO2-e per capita in 2019, compared to 6 t CO2-e on global average.

Quotable carbon inequality stats: - top 0.01% emit more than 2300 t CO2-e per capita in 2019, - global average is 6 t CO2-e - therefore, the top 0.01% emit 2300/6 = 383x more than the global average. - quote - carbon inequality - stats - carbon inequality

#### URL

43. Jan 2023
44. grist.org grist.org
1. Around 40 million homes (or roughly 35 percent of all U.S. houses) use a gas stove to make food.

This is less than I thought it would be.

#### URL

45. s4be.cochrane.org s4be.cochrane.org
1. High-level view of the 3 different types of heterogeneity (clinical, methodological, statistical). I used these definitions as the basis for some Anki cards

#### URL

46. Dec 2022
47. projects.iq.harvard.edu projects.iq.harvard.edu
1. I came here to get the handout for Markov chains mentions in Lecture 31: Markov chains | Statistics 110. Lectures give a great intuition behind the equations, their motivation, and their limitations.

#### URL

48. Nov 2022
49. www.cs.ucr.edu www.cs.ucr.edu
1. Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

Look into what Dr. Miho Ohsaki changed about the clustering subroutine in her work and how it allowed for "more meaningful pattern discovery"

2. Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

Look into Eamonn Keogh's papers that won "best paper awards"

3. http://www.cs.ucr.edu/~eamonn/meaningless.pdf Paper that argues cluster time series subsequences is "meaningless". tl;dr: radically different distributions end up converging to translations of basic sine or trig functions. Wonder if constructing a simplicial complex does anything?

Note that one researcher changed the algorithm to produce potentially meaningful results

#### URL

50. cccrg.cochrane.org cccrg.cochrane.org
1. PDF summary by Cochrane for planning a meta-analysis at the protocol stage. Gives guidance on how to anticipate & deal with various types of heterogeneity (clinical, methodological , & statistical). Link to paper

Covers - ways to assess heterogeneity - courses of action if substantial heterogeneity is found - methods to examine the influence of effect modifiers (either to explore heterogeneity or because there's good reason to suggest specific features of participants/interventions/study types will influence effects of the intervention. - methods include subgroup analyses & meta-regression

2. Statistical heterogeneity is the term given to differences in the effects of interventions and comesabout because of clinical and/or methodological differences between studies (ie it is a consequenceof clinical and/or methodological heterogeneity). Although some variation in the effects ofinterventions between studies will always exist, whether this variation is greater than what isexpected by chance alone needs to be determined.

If the statistical heterogeneity is larger that what's expected by chance alone, then what does that imply? That there's either clinical or methodological heterogeneity within the pooled studies.

What's the impact of the presence of clinical heterogeneity? The statistical heterogeneity (variation of effects/results of interventions) becomes greater than what's expected by chance alone

What's happens if methodological heterogeneity is present? The statistical heterogeneity (variation of effects/results of interventions) becomes greater than what's expected by chance alone

#### URL

51. www.cisco.com www.cisco.com
1. Quadrants I and II: The average student’s scores on basic skills assessments increase by21 percentiles when engaged in non-interactive, multimodal learning (includes using textwith visuals, text with audio, watching and listening to animations or lectures that effectivelyuse visuals, etc.) in comparison to traditional, single-mode learning. When that situationshifts from non-interactive to interactive, multimedia learning (such as engagement insimulations, modeling, and real-world experiences – most often in collaborative teams orgroups), results are not quite as high, with average gains at 9 percentiles. While notstatistically significant, these results are still positive.

I think this is was Thomas Frank was referring to in his YT video when he said "direct hands-on experience ... is often not the best way to learn something. And more recent cognitive research has confirmed this and shown that for basic concepts a more abstract learning model is actually better."

By "more abstract", I guess he meant what this paper calls "non-interactive". However, even though Frank claims this (which is suggested by the percentile increases shown in Quadrants I & II), no variance is given and the authors even state that, in the case of Q II (looking at percentile increase of interactive multimodal learning compared to interactive unimodal learning), the authors state that "results are not quite as high [as the non-interactive comparison], with average gains at 9 percentiles. While not statistically significant, these results are still positive." (emphasis mine)

Common level of signifcances are $$\alpha =.20,~.10,~.05,~.01$$

#### URL

52. stats.stackexchange.com stats.stackexchange.com
1. The random process has outcomes

## Notation of a random process that has outcomes

The "universal set" aka "sample space" of all possible outcomes is sometimes denoted by $$U$$, $$S$$, or $$\Omega$$: https://en.wikipedia.org/wiki/Sample_space

## Probability theory & measure theory

From what I recall, the notation, $$\Omega$$, was mainly used in higher-level grad courses on probability theory. ie, when trying to frame things in probability theory as a special case of measure theory things/ideas/processes. eg, a probability space, $$(\cal{F}, \Omega, P)$$ where $$\cal{F}$$ is a $$\sigma\text{-field}$$ aka $$\sigma\text{-algebra}$$ and $$P$$ is a probability density function on any element of $$\cal{F}$$ and $$P(\Omega)=1.$$

Somehow, the definition of a sigma-field captures the notion of what we want out of something that's measurable, but it's unclear to me why so let's see where writing through this takes me.

## Working through why a sigma-algebra yields a coherent notion of measureable

A sigma-algebra $$\cal{F}$$ on a set $$\Omega$$ is defined somewhat close to the definition of a topology $$\tau$$ on some space $$X$$. They're both collections of sub-collections of the set/space of reference (ie, $$\tau \sub 2^X$$ and $$\cal{F} \sub 2^\Omega$$). Also, they're both defined to contain their underlying set/space (ie, $$X \in \tau$$ and $$\Omega \in \cal{F}$$).

Additionally, they both contain the empty set but for (maybe) different reasons, definitionally. For a topology, it's simply defined to contain both the whole space and the empty set (ie, $$X \in \tau$$ and $$\empty \in \tau$$). In a sigma-algebra's case, it's defined to be closed under complements, so since $$\Omega \in \cal{F}$$ the complement must also be in $$\cal{F}$$... but the complement of the universal set $$\Omega$$ is the empty set, so $$\empty \in \cal{F}$$.

I think this might be where the similarity ends, since a topology need not be closed under complements (but probably has a special property when it is, although I'm not sure what; oh wait, the complement of open is closed in topology, so it'd be clopen! Not sure what this would really entail though 🤷‍♀️). Moreover, a topology is closed under arbitrary unions (which includes uncountable), but a sigma-algebra is closed under countable unions. Hmm... Maybe this restriction to countable unions is what gives a coherent notion of being measurable? I suspect it also has to do with Banach-Tarski paradox. ie, cutting a sphere into 5 pieces and rearranging in a clever way so that you get 2 sphere's that each have the volume of the original sphere; I mean, WTF, if 1 sphere's volume equals the volume of 2 sphere's, then we're definitely not able to measure stuff any more.

And now I'm starting to vaguely recall that this what sigma-fields essentially outlaw/ban from being possible. It's also related to something important in measure theory called a Lebeque measure, although I'm not really sure what that is (something about doing a Riemann integral but picking the partition on the y-axis/codomain instead of on the x-axis/domain, maybe?)

And with that, I think I've got some intuition about how fundamental sigma-algebras are to letting us handle probability and uncertainty.

## Back to probability theory

So then events like $$E_1$$ and $$E_2$$ that are elements of the set of sub-collections, $$\cal{F}$$, of the possibility space $$\Omega$$. Like, maybe $$\Omega$$ is the set of all possible outcomes of rolling 2 dice, but $$E_1$$ could be a simple event (ie, just one outcome like rolling a 2) while $$E_2$$ could be a compound(?) event (ie, more than one, like rolling an even number). Notably, $$E_1$$ & $$E_2$$ are NOT elements of the sample space $$\Omega$$; they're elements of the powerset of our possibility space (ie, the set of all possible subsets of $$\Omega$$ denoted by $$2^\Omega$$). So maybe this explains why the "closed under complements" is needed; if you roll a 2, you should also be able to NOT roll a 2. And the property that a sigma-algebra must "contain the whole space" might be what's needed to give rise to a notion of a complete measure (conjecture about complete measures: everything in the measurable space can be assigned a value where that part of the measurable space does, in fact, represent some constitutive part of the whole).

## But what about these "random events"?

Ah, so that's where random variables come into play (and probably why in probability theory they prefer to use $$\Omega$$ for the sample space instead of $$X$$ like a base space in topology). There's a function, that is, a mapping from outcomes of this "random event" (eg, a role of 2 dice) to a space in which we can associate (ie, assign) a sense of distance (ie, our sigma-algebra). What confuses me is that we see things like "$$P(X=x)$$" which we interpret as "probability that our random variable, $$X$$, ends up being some particular outcome $$x$$." But it's also said that $$X$$ is a real-valued function, ie, takes some arbitrary elements (eg, events like rolling an even number) and assigns them a real number (ie, some $$x \in \mathbb{R}$$).

Aha! I think I recall the missing link: the notation "$$X=x$$" is really a shorthand for "$$X(\omega)=x$$" where $$\omega \in \cal{F}$$. But something that still feels unreconciled is that our probability metric, $$P$$, is just taking some real value to another real value... So which one is our sigma-algebra, the inputs of $$P$$ or the inputs of $$X$$? 🤔 Hmm... Well, I guess it has the be the set of elements that $$X$$ is mapping into $$\mathbb{R}$$ since $$X\text{'s}$$ input is a small omega $$\omega$$ (which is probably an element of big omega $$\Omega$$ based on the conventions of small notation being elements of big notation), so $$X\text{'s}$$ domain much be the sigma-algrebra?

Let's try to generate a plausible example of this in action... Maybe something with an inequality like "$$X\ge 1$$". Okay, yeah, how about $$X$$ is a random variable for the random process of how long it takes a customer to get through a grocery line. So $$X$$ is mapping the elements of our sigma-algebra (ie, what customers actually end up experiencing in the real world) into a subset of the reals, namely $$[0,\infty)$$ because their time in line could be 0 minutes or infinite minutes (geesh, 😬 what a life that would be, huh?). Okay, so then I can ask a question like "What's the probability that $$X$$ takes on a value greater than or equal to 1 minute?" which I think translates to "$$P\left(X(\omega)\ge 1\right)$$" which is really attempting to model this whole "random event" of "What's gonna happen to a particular person on average?"

So this makes me wonder... Is this fact that $$X$$ can model this "random event" (at all) what people mean when they say something is a stochastic model? That there's a probability distribution it generates which affords us some way of dealing with navigating the uncertainty of the "random event"? If so, then sigma-algebras seem to serve as a kind of gateway and/or foundation into specific cognitive practices (ie, learning to think & reason probabilistically) that affords us a way out of being overwhelmed by our anxiety or fear and can help us reclaim some agency and autonomy in situations with uncertainty.

#### URL

53. en.wikipedia.org en.wikipedia.org
1. the moments of a function are quantitative measures related to the shape of the function's graph

Vaguely recall these "uniquely determined" some (but not all) functions. Later on, the article says all moments from $$0$$ to $$\infty$$ do uniquely determine bounded functions. Guess you can't judge a book (or graph) by it's cover; you have to wait moment by moment for it to reveal itself

#### URL

54. Apr 2022
55. digital.autocare.org digital.autocare.org
1. Supply chains were disrupted early in the pandemic, with about half of companies reporting supply chain/sourcing- related disruptions in April 2020 (top three: 47%

creating note for key stat

#### URL

57. Jul 2021
58. engl201.opened.ca engl201.opened.ca
1. ,W¶VXSWR\RXWKHVWDWLVWLFLDQSURJUDPPHUGHVLJQHURUGDWDVFLHQWLVWWRGHFLGHKRZWRWHOOWKHVWRU\

This is a comment on the whole concept really, but the best thing I ever did for myself in terms of gaining a better understanding of data and how to interpret it was to take a research and methods design class, and of course statistics as well. It helped me understand why researchers choose certain ways to represent data, and understand that to the untrained eye, data can be manipulated to seemingly prove almost any point. It is our responsibility to be clear and honest in our presentation of data. Kind of a "with great power comes great responsibility" moment. Because unfortunately, if you throw some statistics around people assume you must know what you are talking about, and often take it at face value without doing their own research, so it is incredibly easy to mislead and misinform the masses in this way.

#### URL

59. Feb 2021
60. www.washingtonpost.com www.washingtonpost.com
1. # The Quest for Truth

The quest for Truth is everywhere and not limited to the economic topics linked here. This is just a topic that started a thought process where I had access to a convenient tool (Hypothesis) to bookmark my thoughts and research.

Primary thought is: The Quest for Truth. Subcategories would provide a structured topic for the thought. In this case the subcategory would be: US Economy, Inflation

#### The TRUTH is a concept comprised of inconsistencies and targets that frequently move.

Targets (data, methods, people, time, semantics, agenda, demographic, motive, means, media, money, status) hold a position in time long enough to fulfill a purpose or agenda. Sometimes they don't consciously change, but history over time shines light and opens cracks in original narrative that leads to new truth's, real or imagined.

#### URL

61. Dec 2020
62. imaging.mrc-cbu.cam.ac.uk imaging.mrc-cbu.cam.ac.uk
1. Rules of thumb on magnitudes of effect sizes

Rules of thumb on magnitudes of effect sizes

#### URL

63. Jun 2020
64. Local file Local file
1. Informal mentorship was captured using the following retrospective question from Wave 3 of the AddHealth data: "Other than your parents or step-parents, has an adult made an important positive difference in your life at any time since you were 14 years old?" Based on this question, I created a binary indicator for mentorship coded 1 if the young person had an informal mentor and 0 if they did not. Respondents were then asked "How is this person related to you?", and given response options like "family,""teacher/counselor,""friend's parent,""neighbor,"and "religious leader.

Defining informal mentorship in the survey data

2. Middle-income subsample 3,158

Middle-income subsample for analysis was 3,158

3. 1. "Middle-income" is defined as anyone living in a household making two-thirds to double the median income (Pew Research Center, 2016). In 1994, the median income for a family of four was $46,757(US Bureau of Statistics, 1996). Thus, "middle-income" families would be those making between$30,860 and $93,514. Because I only have data available in$25,000 increments, I am defining middle-income families as those making between $25,000 and$100,000 a year in Wave 1.

Middle-income = families making $25k-$100k a year in Wave 1

4. Defining low-,middle-, and high-income groupsDue to the limitation in the data described above, all incomes had to be converted in to categorical responses, with the smallest possible category size of $25,000 dollars. This created five categories for all incomes: Defining income groups: under$25k, $25k-$49999, $50k-$74999, $75k-$99999, and $100k+. 5. Wave 1 income was collected as a continuous variable, with an average of$45,728, (N=15,351, SD=$51,616). Low-income respondents (with incomes below$25,000) had an average of $9,837 (N=3,049, SD=4,633). Wave 4 income was recorded as a categorical variable, however, where respondents indicated if they made under$5,000, between $5,000 and$10,000, between $10,000 and$15,000, etc. These categories were of different sizes, getting larger as the income grew larger. Therefore, in order to create comparable measures between Wave 1 and Wave 4, both incomes were converted to 5 groups, (1) household income of less than $25,000, (2) household income of$25,000 to $49,999, (3) household income of$50,000 to $74,000, (4) household income of$75,000 to $99,000, and (5) household income of over$100,000

Upward mobility (dependent variable); data surrounding household incomes of Wave 1 and Wave 4

6. stratum. This sampling method yielded a sample of 20,745 students in 7thto 12thgrade, with oversampling of some minority racialethnic groups, students with disabilities, and twins(Harris, 2018). Data were also collected from the parents of the in-home survey respondents, with an 85% success rate (Chen & Chantala, 2014).Wave 1 participants also reported their home address, which was then linked to a number of state-, county-, and Census tract-level variables from other sources. The present study used the school survey data, the in-home interview data, the parent survey data, and the data that was linked to state, county, and census-tracts, as described above. This study also used data from two subsequent waves of in-home interviews, specifically waves 3 and 4 (no new information relevant to the present study was collected in Wave 2). For each subsequent wave, AddHealth survey administrators recruited from the pool of Wave 1 respondents, no matter if they had responded to any wave since Wave 1. The present study used Wave 1 data for information about the youth’s socioeconomic status, social capital and other related variables. This wave collected from 1994 to 1995, when most respondents were between11 and 19 years old (n=20,745 youth) (Harris, 2013).This study also used information from the third wave of in-home interview data, namely all questions on informal mentoring. This wave wascollected in 2001 and 2002 when the youth (N=15,197) were 18 to 26 years old. The fourth wave of data was collected in 2008 and 2009, when the respondents were 25 to 33 years old (n=15,701). Data from the fourth wave wereused to calculate economic mobility, the key dependent variable for this study.

Data source

Data source

8. Figure 1: Potential Ways MentorsCanPromote Mobility

Figure depicts effects of mentors providing social support and social capital

9. The third function mentors play in promoting upward mobility for young people is the direct effect the provision of social capital (both bridging and bonding capital) has on building blocks of mobility(Ellwood et al., 2016). Bonding capital from a mentor who is also a teacher could foster feelings of school connectedness, which has been demonstrated to lead to academic engagement and ultimately, educational attainment (Ashtiani & Feliciano, 2018; Li, Lerner, & Lerner, 2010). An employer could have a similar effect by providing bonding capital. If a young person feels connected to the workplace or mission of the work place through their mentoring relationships with their employer, they are likely to have higherjob satisfaction and more opportunities for promotion (Ghosh &Reio 2013). Bridging capital can also have a direct effect on key links in the chain. Studies have shown that bridging mentors (commonly teachers and school personnel) were likely to promote educational attainment and employment

Social capital (bridging and bonding) can "foster feelings of school connectedness, which has been demonstrated to lead to academic engagement and ultimately, educational attainment"; similar in workplaces, bonding with mentors in settings can create sense of connectedness with setting overall

10. Those who report feeling emotionally supported have higher rates of academic competence (Sterrett, Jones, Mckee, & Kincaid, 2011) and strong academic outcomes (Wentzel, Russell & Baker, 2016). Additionally, adults who have achieved upward mobility are more likely to report instrumentally supportive relationships than those who were not mobile (Chan, 2017). Clearly, social support has a direct influence on someof thebuilding blocks of mobility

Social support leads to higher rates of academic competence, strong academic outcomes; has a direct influence on some of the building blocks of mobility

11. compensate for the lack of other resources their peers have, such as expansive connected social networks.

Youth from disadvantaged neighborhoods make greater strides than more-resourced peers when mentored by someone outside the family; can potentially compensate for lack of other resources in youth's life

12. A young person's neighborhood context is associated with their chance of being mentored and their chance of being economically mobile. Young people living in under-resourced neighborhoods are also unlikely to be upwardly mobile (Chetty & Hendren, 2016a; Chetty, & Hendren, 2016b; Chetty, Hendren, Kline & Saez, 2014b; Goldsmith, Britton, Reese, & Velez, 2017). Low-income children are more likely to live in neighborhoods with higher crime and drug use (Abelev, 2009). Young people from these neighborhoods are more likelytohave lower tests scores (McCullock & Joshi, 2001), drop out of high school, and be unemployed (Ainsworth, 2002). This neighborhood effect is cumulative: the more time spent in under
• Neighborhood is associated with chance of being mentored
• youth in under-resourced neighborhoods are more unlikely to be upwardly mobile
• in these neighborhoods, likely to have higher crime and drug rates, lower test scores, drop out of high school, and be unemployed
13. young people from more advantaged homes and communities as more likely to have an informal mentor.

Youth in more advantaged homes are more likely to have an informal mentor

14. Black non-Hispanic youth and girls are most likely to be mentored (Bruce & Bridgeland, 2014) as are youth who have a two-parent home with educated parents (Erickson et al., 2009) and not on public assistance (McDonald & Lambert, 2014). Place matters, as having lived in safe neighborhoods (Miranda-Chan, Fruiht, Dubon, Wray-Lake, 2016) and neighborhoods withhigher rates of white, employed individuals not receiving public assistance and living above the poverty line (McDonald & Lambert, 2014) are all associated with a greater chance of reporting a mentor. A young person’s participation in hobbies, organizations, and religious services also leads to higher rates of informal mentorship (Thompson & Greeson, 2017; Schwartz, Chan, Rhodes, & Scales, 2013). Individual qualities such as prosocial behavior (Hagler, 2017), a secure attachment style (Zinn, Palmer, & Nam, 2017), and a likeable personality (Erickson et al., 2009) are associated with having a natural mentor, as does having more friends

Typical mentorship demographics

15. In one study, a low-income child was twice as likely to graduate college when mentored. This is in contrast to previous literature that demonstrates consistent but small associations between informal mentoring and college completion for middle-income children (Reynolds & Parrish, 2018). This suggests that youth from low-income families benefit more from mentorship than those who may have a plethora of positiveresources in their life

Low-income families benefit more from mentorship; one study suggests that mentored low-income children are 2x as likely to graduate college

16. For instance, much attention has been paid to informal mentoring and educational outcomes: mentored youth are more likely to feel connected to their school (Black, Grenard, Sussman, & Rohrbach, 2010), have better grades (Chang et al., 2010), attend college (DuBois & Silverthorn, 2005a; Reynolds & Parrish, 2017) and receive a bachelor’s degree (Miranda-Chan, Fruiht, Dubon, & Wray-Lake, 2016; Erickson, McDonald, Elder, 2009). Cumulatively, these studies, along with a 2018 meta-analysis (Van Dam et al.) suggest a strong and consistent relationship between having an informal mentor and positive educational outcomes.

Informal mentors can result in and influence positive educational outcomes, help promote ability to "feel connected to their school"

17. Literature has established that informal mentoring is most commonly associated with psychosocial outcomes such as lower stress levels, higher life satisfaction, and lower rates of depression (DuBois & Silverthorn, 2005a; Chang et al., 2010; Munson & McMillen, 2009) and socioemotional outcomes, including improved social skills, perceived social support, and higher self-esteem (Van Dam et al., 2018; Miranda-Chan et al., 2016).These associations are strong and consistent across studies, suggesting that informal mentoring is positively correlated with positive psychosocial and socioemotional outcomes.

Informal mentoring is positively correlated with positive psychosocial and socioemotional outcomes

18. Informal mentoring relationships are also more prevalent than formal ones. One study found that 62% of youth had an informal mentoring relationship, compared to just 15% who reportedhaving a formal mentoring relationship(Bruce & Bridgeland, 2014). There are similar differences in prevalence when asking adults if they have mentored young people: 67% of those who reported mentoring someone in the past year did so informally, while only 31% did so through a formal program, (Oosthuizen, 2017). While coming from a low-income family is one of several risk factors associated withlower exposure toinformal mentors, it is clear that many of these youth are still able to identify caring adults in their lives
• 62% of youth had an informal mentoring relationship
• 15% reported formal mentoring relationship
• 67% of adults claimed to have informally mentored someone in last year
• 31% did so in a formal program
• even low-income family youth can identify caring adults in their lives
19. Persistent immobility also disproves the idea of the U.S. being a land of equal opportunity. Since the term "the American Dream" was first coined in 1931, it has become a persistent cultural ethos, a wish list of sorts, with a consistent main tenet being the idea that each generation can achieve more than their parents (Samuel, 2012). Yet we know this tenet of the American Dream is no longer true: the chances that a child earnsmore than their parents has decreased in the past 40 years, especially for low-income families

chances of earning more than parents has decreased in past 40yrs for low-income families

20. he associations between childhood poverty andupward mobility are cumulative: each year of childhood spent in poverty lowers an individual's chances of being upwardly mobile, as they are less likely to be consistently employed or in school

Each year in childhood poverty = less likely to be upwardly mobile, consistently employed/in school

21. Children who experienced any childhood poverty are less likely to be economically mobilethan their middle-income peers(Chetty et al., 2016c; Mitnik et al., 2015) and are more than five times likelier to remain poor in adulthood than to make it to the top income quintile

Any childhood poverty = less likely to be economically mobile, 5x likelier to remain poor in adulthood

22. Even a child who spent just one year in poverty is less likely to have a high school diploma, a key step towards economic success

1 yr of poverty already = less likely to have a high school diploma

23. In 2016, 18% of American children were living in poverty, defined fora household of four as living with an annual income of less than $24,755(Semega, Fontenot & Kollar, 2017). Although this is just one snapshot in time, up to 39% of allAmerican children will experience povertyat some point during theirchildhood(Ratcliffe, 2015). Childhood poverty is linked to low educational attainment, socioemotional issues,and development delays. Poor families are likelier to be exposed to food insecurity, homeless, and unsafe neighborhoods. They are also likelier than their middle-income peers to have poorer health and access to health care In 2016, • 18% of American children lived in poverty • poverty = less than$24,755
• up to 39% of all American children will experience poverty
• childhood poverty is linked to low educational attainment, socioemotional issues, and development delays
• poor families more likely to be exposed to food insecurity, homelessness, and unsafe neighborhoods
• more likely to have poorer health and access to health care
24. There are over 13 million children and adolescents in poverty in the United States today.

13 mil children and adolescents live in poverty in US

25. In 2016, close to one-fifth of American children wereliving in poverty (Semega, Fontenot & Kollar, 2017). These millions of children are likely to remain poor throughout their lives, and are less likely to be upwardly mobile than their middle-income peers (Ratcliffe, 2015; Mitnik, Bryant, Weberb & Grusky, 2015).

1/5 of American children were living in poverty in 2016; likely to remain poor and less likely to be upwardly mobile

26. Low-income youth, however, were less likely to have an informal mentor, and only 45% of those who were mentored had the type that could promote mobility.

Statistical finding: low-income youth likely did not have an informal mentor, and only 45% of those with one were able to have mobility.

#### Annotators

65. outline.com outline.com
1. Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain. Failure to do so is a red flag. As their curiosity pushes them to develop a sense for the business, expect their output to shift from a jumble of false alarms to a sensibly-curated set of insights that decision-makers are more likely to care about.

Analysts have domain expertise or knowledge at least.

2. While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.

More formal and detailed version of above. Besides, the difference of being important and being interesting should be noted too. Maybe search for a thread.

3. For example, not “we conclude” but “we are inspired to wonder”. They also discourage leaders’ overconfidence by emphasizing a multitude of possible interpretations for every insight.

Data analysts are the inspiration team.

4. Analysts are data storytellers. Their mandate is to summarize interesting facts and to use data for inspiration.

This is actually what i do in my reviews too, so i may define myself as a qualitative analyst now.

5. Excellence in analytics: speed The best analysts are lightning-fast coders who can surf vast datasets quickly, encountering and surfacing potential insights faster than those other specialists can say “whiteboard.” Their semi-sloppy coding style baffles traditional software engineers — but leaves them in the dust. Speed is their highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights. The result is that the business gets a finger on its pulse and eyes on previously-unknown unknowns. This generates the inspiration that helps decision-makers select valuable quests to send statisticians and ML engineers on, saving them from mathematically-impressive excavations of useless rabbit holes.

Analysts are more of a digger, they carelessly and fast dig into data, maybe find some directions, which then will be studied elaborately by statisticians and then MLs to create sustainable and automated solutions.

6. Performance means more than clearing a metric — it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demanded.

What machine learners/ AIs do is to scale a statistically rigorous solution to a system-wide, complex problem.

7. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion.

Role of statisticians

#### URL

66. outline.com outline.com
1. The p-value says, “If I’m living in a world where I should be taking that default action, how unsurprising is my evidence?” The lower the p-value, the more the data are yelling, “Whoa, that’s surprising, maybe you should change your mind!”

In a simpler context, it means the occurrence of default (null) situation is of very low probability.

#### URL

67. Dec 2019
68. www.washingtonpost.com www.washingtonpost.com
1. “Every data point was altered to present the best picture possible,”Bob Crowley | Lessons Learned interview | 8/3/2016Tap to view full document Bob Crowley, an Army colonel who served as a senior counterinsurgency adviser to

#### URL

69. May 2019
70. www.reddit.com www.reddit.com
1. Brook Lopez this season had more blocks than Kevin Garnett had in his best season and more 3 pointers than Kobe Bryant had in his best season...

Mindblowing

#### URL

71. www.gwern.net www.gwern.net
1. statistical modelling problems - relevant to measurement

#### URL

72. Apr 2019
73. statistics.laerd.com statistics.laerd.com
1. ANOVA Table

#### URL

74. statistics.laerd.com statistics.laerd.com
1. There are two tests that you can run that are applicable when the assumption of homogeneity of variances has been violated: (1) Welch or (2) Brown and Forsythe test. Alternatively, you could run a Kruskal-Wallis H Test. For most situations it has been shown that the Welch test is best. Both the Welch and Brown and Forsythe tests are available in SPSS Statistics (see our One-way ANOVA using SPSS Statistics guide).

ANOVA is robust against violation of the assumption of equal variances, but...

2. However, platykurtosis can have a profound effect when your group sizes are small. This leaves you with two options: (1) transform your data using various algorithms so that the shape of your distributions become normally distributed or (2) choose the nonparametric Kruskal-Wallis H Test which does not require the assumption of normality.

ANOVA is robust against violation of normality, but...

#### URL

75. Mar 2019
76. statistics.laerd.com statistics.laerd.com
1. Testing for Normality using SPSS Statistics

#### URL

77. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
1. We performed some manipulation checks to examine the internal validity of the perceptual-cognitive skill tests and any learning effects as a result of watching the same video clips multiple times

#### URL

78. Feb 2019
79. www.sciencedirect.com www.sciencedirect.com
1. Due to our emotional distress measure having little prior validation, and our physical distress measure being entirely new, we first provide data to support the appropriateness of the two measures.

An example of survey validation using Crombach's alpha.

#### URL

80. statistics.laerd.com statistics.laerd.com
1. You may believe that there is a relationship between 10,000 m running performance and VO2max (i.e., the larger an athlete's VO2max, the better their running performance), but you would like to know if this relationship is affected by wind speed and humidity (e.g., if the relationship changes when taking wind speed and humidity into account since you suspect that athletes' performance decreases in more windy and humid conditions).

An example of partial correlation.

#### URL

81. Nov 2017
82. blog.medium.com blog.medium.com
1. Developers are an important demographic. Apple says they are the biggest segment of Macbook Pro users, which means they spend a lot of money. And they’re a demographic underserved by Chromebooks today.

#### URL

83. docs.statwing.com docs.statwing.com
1. Heteroscedasticity

Heteroscedasticity is a hard word to pronounce, but it doesn't need to be a difficult concept to understand. Put simply, heteroscedasticity (also spelled heteroskedasticity) refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it.

#### URL

84. Apr 2017
85. bangordailynews.com bangordailynews.com
1. The annual drop in Maine wood demand since 2014 would fill that imaginary 1,770-mile caravan. The loss equals about 350 fewer truckloads of wood a day, every day of the year.

#### URL

86. Mar 2017
87. bangordailynews.com bangordailynews.com
1. A typical acre of blueberry barrens will yield about 2,000 to 4,000 pounds of berries, depending on pollination and other factors.

#### URL

88. Feb 2017
1. Toxic air now kills almost as many people as high cholesterol and even more than excessive salt or being overweight.

#### URL

90. Jan 2017
91. static1.squarespace.com static1.squarespace.com
1. especially those of figure and number, of which men have so clear and dis-tinct ideas

Add in some Lemos here as well. And some Mark Twain.

#### URL

92. Feb 2016
93. bangordailynews.com bangordailynews.com
1. He expects that the logging project near Quimby’s land will likely generate about $755,250 at the state’s average sale price,$50.35 per cord of wood. The land has about 1,500 harvestable acres that contain about 30 cords of wood per acre, or 45,000 cords, but only about a third of that will be cut because the land is environmentally sensitive, Denico said. The Bureau of Parks and Lands expects to generate about $6.6 million in revenue this year selling about 130,000 cords of wood from its lots, Denico said. Last year, the bureau generated about$7 million harvesting about 139,000 cords of wood. The Legislature allows the cutting of about 160,000 cords of wood on state land annually, although the LePage administration has sought to increase that amount.

#### URL

94. Jan 2016
95. blogs.scientificamerican.com blogs.scientificamerican.com
1. P(B|E) = P(B) X P(E|B) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(B|E) means the probability of B if E is true, and P(E|B) is the probability of E if B is true.
2. The probability that a belief is true given new evidence equals the probability that the belief is true regardless of that evidence times the probability that the evidence is true given that the belief is true divided by the probability that the evidence is true regardless of whether the belief is true. Got that?
3. Initial belief plus new evidence = new and improved belief.

#### URL

96. Oct 2013
97. rhetoric.eserver.org rhetoric.eserver.org
1. The things that happen by chance are all those whose cause cannot be determined, that have no purpose, and that happen neither always nor usually nor in any fixed way.

This is not how statistic work.