• Table
  • Interrelationship of wealth and emissions per capita. //
• It is clear that we have an urgent need
• to bend the curve of emissions of elites

Further arguments that Ed25519 is less vulnerable to - cache-timing attacks - hyperthreading attacks - other side-channel attacks that rely on leakage of addresses through CPU cache Also boasts - no secret branch conditions (no conditional branches based on secret data since pattern of jumps is predictable)

Predicable because underlying process that generated it isn't a black box?

Could ML (esp. NN, and CNN) be a parallel? Powerful in applications but huge risk given uncertainty of underlying mechanism?

Need to read papers on this

An argument why Ed25519 signature alg & Curve 25519 key exchange alg is more secure; less vulnerable to side attacks since the process that generates is have been purportedly verified and extensively documented.

The epic meaning of "Bend the Curve" is saving civilization, co-creating a future worth living in the next few years, averting disaster.

On a personal level the transformation of the individual also conveys epic meaning.

This is a key leverage point strategy for Stop Reset Go for Rapid Whole System Change (RWSC) strategy. As argued by Kevin Anderson https://youtu.be/mBtehlDpLlU, the wealthy are a crucial subculture to target and success can lead to big decarbonization payoffs.

The key is to leverage what contemplative practitioners and happiness studies both reveal - after reaching a specific level of material needs being met, which is achievable for staying within planetary boundaries, we don’t need any more material consumption to be happy. We need an anti-money song: https://youtu.be/_awAH-JJx1kamd and enliven Martin Luther King Junior’s quote aspirational: the only time to look down at another person is to give them a hand up. Educate the elites on the critical role they now play to solve the double problem of i equality and runaway carbon emissions.

All three variables - population growth, consumption and waste must be minimized simultaneously in order to bend the curve back to a safe operating safe for humanity

These are umbrella categories that can allow for the classification of vast numbers of existing transition projects. With the use of disaggregated planetary boundaries, doughnut economics framework, Inner and Outer transformation, and Bend-the-Curve gamification, the impacts of each type of operation can be measured.

Gamification will play a critical role to tap into the human psychology that will encourage proactive action. Bend-the-Curve is the glocal game proposed as a way to mobilize ordinary citizens aggregate community scale response teams.

As part of this gamification, a private Transform application within the public and open Indyweb can facilitate individual inner transformation, synchronize that to individual outer (behavior) transformation and synchronize that to collective inner and outer transformation at the respective community collective scale and finally aggregating all community impacts, to the global collective transformation scale. Built in data privacy of the Indyweb insures that everyone can contribute data to the aggregator in a completely anonymous way. All of this is designed to operationalize Donella Meadow's insight that inner transformation of worldviews, paradigms and value systems is the most powerful of all leverage points.

The Tipping Point Festival can introduce the Bend-the-Curve (BtC) gamification to engage as many cities, towns, rural communities and bioregions as possible. A 3 year research program to dis-aggregate planetary boundaries can allocate a fairshare of local biophysical targets each city, town, rural community and bioregion must aim to achieve if we as a civilization are to meet the 1.5 deg C target, as well as other Anthropocene and planetary boundary targets.

Doughnut economic framework can be adopted immediately and educated across all communities to plant seeds of local change actor chapters who can start their own local doughnut economies and begin reshaping their local economy into circular bio WEconomies.

When the dis-aggregated planetary boundary metrics are available, then each community can adopt and aim to bend their local curve, in order that we altogether bend the global curves back to a safe operating space.

it may be questionable whether we are able to develop highly accurate targets, but even if we are close enough, the greater value is to allow citizens to have a tangible and compelling and measurable reason to work together, organize and mitigate our human impacts in a systematic way. In this way, we can expose the hyperthreat by breaking it down into digestable, identifiable pieces that are cognitively more accessible and can lodge into the salience landscape of the individuals of a community.

ReconfigBehSci. (2022, January 28). RT @Gab_H_R: Ok this is not looking good. Look at Brazil’s death curve following cases. This is not a good sign. Https://t.co/gvgdQOyMAU [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1487128383301599234

Curving grades remains an unquestioned practice, in some contexts. Despite all the debunking which has been done since Terman's (in)famous work...

ReconfigBehSci on Twitter: ‘RT @AdamJKucharski: Worth reading thread below – and here’s current epidemic curve for comparison: Https://t.co/N6LCEssp1Z https://t.co/yDC…’ / Twitter. (n.d.). Retrieved 29 July 2021, from https://twitter.com/SciBeh/status/1409530195099328514

Aletti, G., Crimaldi, I., & Saracco, F. (2020). A model for the Twitter sentiment curve. ArXiv:2011.05933 [Physics]. http://arxiv.org/abs/2011.05933

As Hall points out in Where Is My Flying Car? there is a strong argument to be made that the industrial revolution was a social chain reaction that had already set off an exponential improvement process on its way to 'singularity', this was disrupted in the 20th century however. But prior to that it had been ongoing for 300 years at a steady average rate of 7% more usable power annually.

Lalwani, P., Fansher, M., Lewis, R., Boduroglu, A., Shah, P., Adkins, T. J., … Jonides, J. (2020, November 8). Misunderstanding “Flattening the Curve”. https://doi.org/10.31234/osf.io/whe6q

There was a steep learning curve involved with using ECCO Pro. Reminds me of Roam, which also has a steep learning curve, but then it feels like it's worth it.

Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.

COVID-19: Doing It Right. (2020, July 3). Open Mind. https://tamino.wordpress.com/2020/07/03/covid-19-doing-it-right/

Barro, R. J. (2020). Non-Pharmaceutical Interventions and Mortality in U.S. Cities during the Great Influenza Pandemic, 1918-1919 (Working Paper No. 27049; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27049

Bethune, Z. A., & Korinek, A. (2020). Covid-19 Infection Externalities: Trading Off Lives vs. Livelihoods (Working Paper No. 27009; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27009

Harris, J. E. (2020). The Coronavirus Epidemic Curve is Already Flattening in New York City (Working Paper No. 26917; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26917

Modelling the Distributional Impact of the COVID-19 Crisis. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13235/

Lockdown Strategies, Mobility Patterns and COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13293/

Salvador, C., Berg, M., Yu, Q., Alvaro, S., & Kitayama, S. (2020, April 13). Relational mobility predicts the increased speed of the spread of COVID-19: A 37 country study. https://doi.org/10.31234/osf.io/gwpj3

Larry Brilliant on How Well We Are Fighting Covid-19. (n.d.). Wired. Retrieved July 18, 2020, from https://www.wired.com/story/larry-brilliant-on-how-well-are-we-fighting-covid-19/

Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6

Zickfeld, J., Schubert, T. W., Herting, A. K., Grahe, J. E., & Faasse, K. (2020, April 16). Predictors of Health-Protective Behavior and Changes Over Time During the Outbreak of the COVID-19 Pandemic in Norway. https://doi.org/10.31234/osf.io/6vgf4

Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40(6), 998–1009. https://doi.org/10.1002/ejsp.674

Perencevich, E. N., Diekema, D. J., & Edmond, M. B. (2020). Moving Personal Protective Equipment Into the Community: Face Shields and Containment of COVID-19. JAMA. https://doi.org/10.1001/jama.2020.7477

Countries should seize the moment to flatten the climate curve. (2020, May 21). The Economist. Retrieved June 4, 2020, from https://www.economist.com/leaders/2020/05/21/countries-should-seize-the-moment-to-flatten-the-climate-curve

Yu, Q., Salvador, C., Melani, I., Berg, M., & Kitayama, S. (2020, May 14). The lethal spiral: Racial segregation and economic disparity jointly exacerbate the COVID-19 fatality in large American cities. https://doi.org/10.31234/osf.io/xgbpy

In economics, this same concept is known as the J Curve (https://www.wikiwand.com/en/J_curve)

Roland, L. T., Gurrola, J. G., Loftus, P. A., Cheung, S. W., & Chang, J. L. (2020). Smell and taste symptom‐based predictive model for COVID‐19 diagnosis. International Forum of Allergy & Rhinology, alr.22602. https://doi.org/10.1002/alr.22602

Expert comments about herd immunity | Science Media Centre. (n.d.). Retrieved April 14, 2020, from https://www.sciencemediacentre.org/expert-comments-about-herd-immunity/

Coronavirus infections have peaked in much of the rich world. (n.d.). The Economist. Retrieved April 27, 2020, from https://www.economist.com/graphic-detail/2020/04/17/coronavirus-infections-have-peaked-in-much-of-the-rich-world?fsrc=scn/tw/te/bl/ed/dailychartcoronavirusinfectionshavepeakedinmuchoftherichworldgraphicdetail

Thunstrom, L., Newbold, S., Finnoff, D., Ashworth, M., & Shogren, J. F. (2020). The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19 (SSRN Scholarly Paper ID 3561934). Social Science Research Network. https://doi.org/10.2139/ssrn.3561934

The normal distribution in r

The continuity between these new modular features and the rest of the DAW’s workflow probably has unexpected consequences. Before getting information about BWS3, one might have thought that the “Native Modular System” promised since the first version might still be an add-on. What the marketing copy around this “killer feature” makes clear, it’s the result of a very deliberate process from the start and it’ll make for a qualitatively different workflow.

I wrote this sentence. Two coauthors, three peer reviewers, and an editor all read it multiple times. No one ever asked for it to be changed.

I think this paper and these data could be extremely useful for psychologists, but I also think this paper needs at least one more analysis: estimating effect sizes by research area, controlling for publication bias.

It's very hard to interpret these estimates given good evidence and arguments that researchers and journals select for p < .05. I think it's safe to assume that all these estimates reported in this preprint are bigger than the true averages (Simonsohn, Nelson, & Simmons, 2014).

One approach to estimating "selection bias adjusted" effects would be to estimate the effect size for each research area using the code provided in the p-curve effect size paper supplements (http://www.p-curve.com/Supplement/). You could estimate confidence intervals or percentiles using bootstrapping procedures or write code to estimate the lower and upper bounds using the same methods to estimate the average effect.

This approach assumes the p-values all test the hypothesis of interest and don't suffer from unique selection biases (see Selecting p Values in Simonsohn, Nelson, & Simmons, 2014, p. 540).

Hope this helps make the paper better!