It may refer to an internal, abstract, conceptual or emotional (invisible) reality.

This text uses insights gleaned from the fields of anthropology, religious studies, depth psychology, literature, and archaeology to explore the fundamental wisdom of the world’s great mythological traditions.

Twitter. November 2023. Page Version ID: 1187856185. URL: https://en.wikipedia.org/wiki/Twitter (visited on 2023-12-01).

Caroline Delbert. Some People Think 2+2=5, and They’re Right. Popular Mechanics, October 2023. URL: https://www.popularmechanics.com/science/math/a33547137/why-some-people-think-2-plus-2-equals-5/ (visited on 2023-11-24).

Ruta Butkute. The dark side of voluntourism selfies. June 2018. URL: https://kinder.world/articles/you/the-dark-side-of-voluntourism-selfies-18537 (visited on 2023-11-24).

Shannon Bond. Elon Musk wants out of the Twitter deal. It could end up costing at least $1 billion. NPR, July 2022. URL: https://www.npr.org/2022/07/08/1110539504/twitter-elon-musk-deal-jeopardy (visited on 2023-11-24).

Document file format. August 2023. Page Version ID: 1170388374. URL: https://en.wikipedia.org/w/index.php?title=Document_file_format&oldid=1170388374 (visited on 2023-11-24).

Matt Binder. The majority of traffic from Elon Musk's X may have been fake during the Super Bowl, report suggests. February 2024. Section: Tech. URL: https://mashable.com/article/x-twitter-elon-musk-bots-fake-traffic (visited on 2024-03-31).

Caroline Delbert. Some People Think 2+2=5, and They’re Right. Popular Mechanics, October 2023. URL: https://www.popularmechanics.com/science/math/a33547137/why-some-people-think-2-plus-2-equals-5/ (visited on 2023-11-24).

Julia Evans. Examples of floating point problems. January 2023. URL: https://jvns.ca/blog/2023/01/13/examples-of-floating-point-problems/ (visited on 2023-11-24).

Caroline Delbert. Some People Think 2+2=5, and They’re Right. Popular Mechanics, October 2023

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When you were a kid, you probably asked your parents this question at some point about things you were told to do, and you probably got answers varying from “Because I said so” to “Remember, if you finish up soon, you’ll have time to play”.  These responses serve as motivation for you to do that thing; one is an order and the other is a promise.

Fixed mindsets can take over our learning when we become attached to a score or result such as a grade.

batch

costs remain high

It is important to note that language ideologies do not always have negative impacts.

It is important to note that language ideologies do not always have negative impacts.

We have to be aware that we are always making these simplifications, try to be clear about what simplifications we are making, and think through the ethical implications of the simplifications we are making.

While we don’t have direct access to all the data ourselves, we can imagine that different definitions would lead to different results. And there isn’t a “best” or “unbiased” definition we should be using, since all definitions are simplifications that will help with some tasks and hurt with others.

As a college student, you may be asked to begin research papers with a synthesis of the sources.  Your primary purpose is to show readers that you are familiar with the field and are qualified to offer your own opinions.  But your larger purpose is to show that in spite of all this wonderful research, no one has addressed the problem in the way that you intend to in your paper.  This gives your synthesis a purpose and even a thesis of sorts. Because each discipline has specific rules and expectations, you should consult your professor or a guidebook for that specific discipline if you are asked to write a review of the literature and aren’t sure how to do it.

In contrast, a thesis-driven synthesis not only combines information from multiple sources, but also uses that information to support a central claim or argument. Here, you evaluate and interpret the sources to develop your own perspective or theory about the topic.

A synthesis can serve different purposes, depending on the assignment. In a background synthesis, your goal is to collect and organize information from various sources by topic or theme, presenting an overview of what is known about a subject. This type does not require an argument or thesis—it simply helps readers understand the current state of research or information.

(1)  Accurately reports information from the sources using different phrases and sentences; (2)  Organized in such a way that readers can immediately see where the information from the sources overlap;. (3)  Makes sense of the sources and helps the reader understand them in greater depth.

The basic research report (described below as a background synthesis) is a common document in the business world.  Whether one is proposing to open a new store or expand a product line, the report will synthesize information and arrange it by topic rather than by source.  Whether you want to present information on child rearing to a new mother, or details about your town to a new resident, you’ll find yourself synthesizing too. And just as in college, the quality and usefulness of your synthesis will depend on your accuracy and organization.

Whenever you report to a friend about a film or podcast, you engage in synthesis.  People synthesize information naturally to help other see the connections between things they learn;  for example, you have probably stored up a mental data bank of the various descriptions you’ve heard about particular professors. If your data bank contains several positive descriptions, you might synthesize that information and use it to enroll in a class from that professor.  Synthesis is related to but not the same as classification, division, or comparison and contrast.  Instead of attending to categories or finding similarities and differences, synthesizing sources is a matter of pulling them together into some kind of harmony. Synthesis searches for links between materials for the purpose of constructing a thesis or theory.

At its most basic level, a synthesis involves combining two or more summaries, but synthesis writing is more difficult than it might at first appear because this combining must be done in a meaningful way, and the final essay must generally be thesis-driven.  In composition courses, “synthesis” commonly refers to writing about printed texts, drawing together particular themes or traits that you observe in those texts, organizing the material from each text according to those themes or traits, and developing your own thesis or theory.  Sometimes, you may be asked to synthesize your own ideas with those of the texts you have been assigned. In your other college classes, you’ll probably find yourself synthesizing information from graphs and tables, pieces of music, and artworks as well.

What if someone told you that you couldn’t pick up a paintbrush unless you were already a great artist? What if someone said you could only swim in the pool if you were an Olympic-level swimmer? Or that you couldn’t make pasta in the kitchen because you’re not yet a 5-star chef? You would immediately know that such high standards are ridiculous. Then why do many of us have such fear of learning languages ‘imperfectly’?

When you finalize your conclusion, make sure your text is not too repetitive. While your goal is to reintroduce your argument, you don’t want to bore the reader with the exact same sentences you included in your introduction; instead, reiterate your thesis using your new perspective of the topic.

Reintroduce the argument introduced in your thesis statement. Reiterate the key points of your research. Offer some forecasts for the future (example: “Hopefully now with a clearer understanding about free soloing and the rock-climbing community, others might understand the draw to such a seemingly risky sport…”).

The conclusion is your opportunity to summarize the essay and hopefully spur the reader to want to learn more about the topic. Be sure to clearly reiterate the thesis statement. In your introduction, you may have laid out what would be covered in the essay. Offer a sentence or two reiterating what was learned about those topic areas. Finally, work to avoid adding any new information and questions in this final section of your writing.

Begin with a topic sentence. Using one of the five Ws or H questions here will remind you and your readers what you will focus on in this paragraph. Introduce your sources in a sentence or two to summarize what the information revealed about your topic. Include a direct quote using P.I.E. and reflect on what the source illuminated about your question.

The main purpose of the body paragraphs is to inform the target audience about the background/significance of your topic or the answers to the 5 Ws and H driving questions that you focused your research on. Share some interesting facts, go into the possibly unknown details, or reflect common knowledge in a new light to make readers intrigued. Body paragraphs should discuss the inquiry process you followed to research your topic

Define the topic. Provide short background information. Introduce who your intended audience is. State what your driving research question is. Create a thesis statement by identifying the scope of the informative essay (the main point you want your audience to understand about your topic).

Then, introduce the topic with its background in a couple of sentences. The writer will then end the paragraph with a powerful thesis statement, which points to the necessity of topic research. The writer’s goal is to do everything possible to lure the audience’s interest in the initial paragraph.

The initial stage is an introduction, which should start with the sound hook sentence to engage the reader in what a writer plans to share. One example is: “A community is generally defined by people in a group who live together in a particular area, or a group of people who are considered a unit because of their shared interests or background.”

Some events and trends are too recent to appear in Tier One sources, which tend to be highly specific, and sometimes you need a more general perspective on a topic. Thus, Tier Two sources can provide quality information that is more accessible to non-academics. There are three main categories. First, official reports from government agencies or major international institutions like the World Bank or the United Nations; these institutions generally have research departments staffed with qualified experts who seek to provide rigorous, even-handed information to decision-makers. Second, feature articles from major newspapers and magazines like the New York Times, Wall Street Journal, London Times, or The Economist are based on original reporting by experienced journalists (not press releases) and are typically 1500+ words in length. Third, there are some great books from non-academic presses that cite their sources; they’re often written by journalists. All three of these sources are generally well researched descriptions of an event or state of the world, undertaken by credentialed experts who generally seek to be even-handed.

These are sources from the mainstream academic literature: books and scholarly articles. Academic books generally fall into three categories: (1) textbooks written with students in mind, (2) monographs which give an extended report on a large research project, and (3) edited-volumes in which each chapter is authored by different people. Scholarly articles appear in academic journals, which are published multiple times a year in order to share the latest research findings with scholars in the field. They’re usually sponsored by some academic society. To get published, these articles and books had to earn favorable anonymous evaluations by qualified scholars.

The Informative Research Report draws primarily from resources found in tiers 1 and 2, according to the research table in Writing in College:

Table: Research Source Tiers from Writing in College

The point of an informative essay is not to convince others to take a certain action or stance; that role is expressly reserved for persuasive essays. Instead, the main objective is to highlight specific information about your topic. In this project, you may be asking “after researching general aspects about my topic, what do I want others to understand about it?” Of course, if your informative essay is interesting enough, it may move readers to learn more about the subject, but they’ll have to come to that on their own, thanks to the wealth of interesting information you present.

The Five W’s and How, Image by Gerd Altmann from Pixabay. The purpose of an informative essay, sometimes called an expository essay, is to educate others on a certain topic. Typically, these essays aim to answer the five Ws and H questions: who, what, where, when, why, and how. For this essay, you will focus on one or two driving questions about your topic, which will drive your research and help you reach a conclusion. The question can be one that emerged from your Exploratory Essay or it can be a brand-new question about your topic that you are interested in researching.

Sometimes students are asked to write an informative research report, which is a different type of document than the exploratory essay, which we will cover in the next chapter. The Informative Research Report is a report that relays the results of a central research question in an organized manner through more formal sources. These resources could include Google Scholar, library catalogs and academic article databases, websites of relevant agencies, and Google searches using (site: *.gov or site: *.org). A report is written from the perspective of someone who is seeking to find specific and in-depth information about a certain aspect of a topic.

symbolize human experience and embody the spiritual values of aculture.

Can you think of an example of pernicious ignorance in social media interaction?

Think for a minute about consequentialism. On this view, we should do whatever results in the best outcomes for the most people. One of the classic forms of this approach is utilitarianism, which says we should do whatever maximizes ‘utility’ for most people. Confusingly, ‘utility’ in this case does not refer to usefulness, but to a sort of combo of happiness and wellbeing. When a utilitarian tries to decide how to act, they take stock of all the probable outcomes, and what sort of ‘utility’ or happiness will be brought about for all parties involved. This process is sometimes referred to by philosophers as ‘utility calculus’. When I am trying to calculate the expected net utility gain from a projected set of actions, I am engaging in ‘utility calculus’ (or, in normal words, utility calculations).

Can you think of an example of pernicious ignorance in social media interaction? What’s something that we might often prefer to overlook when deciding what is important?

Dates turn out to be one of the trickier data types to work with in practice. One of the main reasons for this is that what time or day it depends on what time zone you are in.

How sound data gets turned into sound waves: The numerical sound data is turned into an electrical current through a wire, which creates an electromagnetic force which then pushes and pulls on a diaphragm inside a speaker to create physical sound waves. Microphones do this process in exactly the reverse, the sound waves in the air make the speaker diaphragm go back and forth, making an electric current in the wire which gets measured and saved by the computer.#

By the late 1960s, an estimated 26 families had been displaced by urban renewal projects in Maywood, 88% of which were families of color.

By the late 1960s, an estimated 22,950 families had been displaced by urban renewal projects in Chicago, 64% of which were families of color.

All of this resonated widely, enabling the Nazis to win 37 percent of the vote in the election of 1932

Experience soon showed that Japan’s concern was far more for Asia’s resources than for its liberation and that Japanese rule exceeded in brutality even that of the Europeans.

Non-Russian nationalists in Ukraine, Poland, Muslim Central Asia, and the Baltic region demanded greater autonomy or even independence.

Women were urged to leave the factory work they had taken up during the war and return to their homes, where they would not compete against returning veterans for “men’s jobs.”

What data types might be used to represent that data on a computer?

it is not okay to drive on that road (and over the person).

computer generated characters in motion pictures such as Avatar, the Lord of the Rings, and popular Pixar animations where the animated characters replicate gestures made by real human actors.

While the frontpage of the printed version of the New York Times or China Daily is the same for all readers, the frontpage of the online version is different for each user. The algorithms that determine the content that you see are based on AI.

Implicit state is the Enemy

The funny part is that none of this made the CLI worse for humans.

Whether or not this specific bet pays off, the underlying argument that the next meaningful leap in AI capability requires moving beyond language modeling is increasingly hard to dismiss.

The clustering of capital and talent around this problem is itself a signal. The applications that most clearly benefit from world models are those where LLMs have struggled most.

AMI Labs is not building a product for immediate deployment. This is a fundamental research effort, likely measured in years before commercial applications emerge.

LLMs have no grounded understanding of the physical world. They model the statistical distribution of language about reality, not reality itself.

You have to have people that have the ability to rethink the workflow at a scale that AI can execute, versus at a scale that humans can execute.

The government has so far favoured a pro-innovation, sector-led approach, prioritising voluntary principles over hard regulation.

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

model alignment alone does not reliably guarantee the safety of autonomous agents

verifiers and observer models inside the action-memory loop reduce silent failure and information leakage while remaining vulnerable to misspecification.

we use the distance preference characterized by these centers to score keys according to their positions, and also leverage Q/K norms as an additional signal for importance estimation

This class of bug is insidious because it evades every layer of defense. It will not be caught in development testing — who runs a test for 50 days? It will not be flagged in code review — the logic looks perfectly reasonable.

Looking at the code and having opinions on architecture is seen as just as 'bad' as calling a compiled C module from an interpreted language was seen back in the day... it's not bad, it's actually quite practical, but it violates some strange 'purity'.

The AI is actually very good at this, especially if you have a conversation with it beforehand. That's what Ask mode is for.

Bad software is a decision you make. You need to own it. You should do better.

Looking under the hood is cheating. You're only supposed to have vague conversations with the machine about what it's doing.

I feel confident, though, that the slippery feeling people associate with AI products is a solvable problem, and the solution looks more like thoughtful interface design than better models. The models will keep improving on their own. The harder work is building the structure around them so that their output feels reliable, legible, and trustworthy.

When you delegate an issue to an agent in Linear, the delegation is visible. There's a person who set the agent loose within that system, and that person is accountable for the outcome. You design the environment well, you let the agent run, and you own what it produces.

The more important work happens before the agent even starts. An agent operating inside a well-designed system already has the context and constraints it needs to do good work. In Linear, that means project plans, issue backlogs, code, and documentation. These all shape what the agent does and how it does it.

An agent cannot be held accountable. I think about this principle most. The instinct to put a human in the loop is understandable, but taken literally, it can mean a person approving every step before anything moves forward. The human becomes a bottleneck, rubber-stamping work rather than directing it, and you lose much of what makes agents valuable in the first place.

The first interface that spread for AI tools was the chat window. That makes sense. When you don't know what something can do, the safest approach is to let people ask. A conversation feels familiar, it stretches across many situations, and it doesn't force a specific structure up front.

Non-deterministic software breaks the contract. When outcomes can vary, sometimes wildly, based on what someone types into the same chat window, designing for reliability becomes genuinely harder. This slippery feeling is the design problem of this era, and it almost always traces back to the interface rather than the language model—which means it belongs to designers, not researchers.

AI is a way to level the playing field, for sure! Successful writers have always operated with a lot of support around them, but not everyone has access to those resources.

When I sit down to write a piece, and before I even write a word, I have the agent interview me. It asks questions to draw out what I'm thinking about the topic.

It has a panel of critics who tear my work apart from different angles—skills I wrote to invoke certain kinds of feedback, whether it's for length, pacing, or the soundness of the argument.

OpenAI just raised $122 billion at an $852 billion valuation. That's the largest private funding round ever.

AI is no longer limited by ideas. It's limited by money, compute, and who can scale fastest.

Sam Altman has reportedly told staff that Spud could "really accelerate the economy"

both companies are hinting that these models are a real step forward, not just small upgrades.

Gemma points in the opposite direction: smaller models, local compute, more ownership.

A founder in LA reportedly scaled Medvi toward $1.8B in annual sales with basically one full-time employee.

And once models get good at that, the question stops being whether they can make beautiful images. It becomes whether people still notice when something was never real to begin with.

Most people talk about OpenAI like it's basically 'owned by Microsoft,' but the actual cap table is much more spread out.

The first wave of image models was mostly about making cool-looking images. This next phase is about making ordinary things look real.

We are building a world where machines write the code, machines choose the dependencies, and machines ship the updates. The AI agents are building the software. If we don't secure the supply chain they rely on, the AI agents are cooked.

Socket, an a16z portfolio company, detected the malicious dependency in the Axios attack within 6 minutes of its publication. That's roughly 63,000 times faster than the industry average.

The autonomous coding agents now entering production can install dependencies, execute builds, and open pull requests without a human ever touching the keyboard. They optimize for 'does this work?' not 'is this safe?'

Hallucinated packages are the sleeper threat. LLMs regularly invent package names that don't exist. One study found that nearly 20% of AI-recommended packages were fabrications, and 43% of those hallucinated names appeared consistently across queries.

AI agents select known-vulnerable dependency versions 50% more often than humans. Worse, the vulnerable versions they pick are harder to fix, requiring major-version upgrades far more frequently.

Talent density : the biggest prizes in capitalism attract the best minds in the field. These are the fastest growing software companies in history.

At this rate, the phone in your pocket will run today's frontier models before you upgrade it.

In 23 months, the same capability that needed 1.8 trillion parameters now fits in 4 billion parameters. A 450x compression.

Within three to four months, you can run a model with similar performance on your laptop; 23 months later, you can run the same model on your phone.

Someone who builds premium dating apps, let's say, might use AI coding tools to create in one day what used to take three days. That means the worker is more productive. The worker's employer, spending the same amount of money, can now get more output. So then will the employer want more employees or fewer?

We need, like, a Manhattan Project to collect this... Fields that are not exposed now will become exposed in the future, so you just want to track these statistics across the entire economy.

Exposure alone is a completely meaningless tool for predicting displacement

in the past year Huawei has overtaken Nvidia as the leading source of AI computing power in China, at least in terms of rated FLOP/s

We estimate that as of the end of 2025, Chinese companies collectively own just over 5% of the cumulative computing power of the leading AI chips sold in recent years

Many frontier AI developers, including Anthropic and OpenAI, acquire almost all of their compute from hyperscalers and other cloud providers.

Google holds the equivalent of around 5 million Nvidia H100 GPUs in compute capacity, roughly 25% of the world's total!

We estimate that over 60% of global AI compute (in terms of total computing power) is owned by the five US hyperscalers, led by Google.

For higher-interactivity scenarios, execution time for MoE models is bound by expert weight load time. By splitting, or sharding, the experts across multiple GPUs across NVL72 nodes, this bottleneck is reduced, improving end-to-end performance.

NVIDIA yields unmatched inference throughput across the broadest range of workloads, from massive LLMs to advanced vision language models, to generative recommender systems and more, on industry-standard benchmarks.

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications.

NVIDIA was the first and only platform to submit DeepSeek-R1 results on MLPerf Inference when the benchmark debuted last year.

This means 2.7x more tokens from the same GB300 NVL72-based infrastructure and power footprint, reducing the cost to manufacture each token by more than 60%.

Using vLLM high-throughput LLM serving on DGX Spark provides a high-performance platform for the largest Gemma 4 models

The E4B and E2B are the newest edition of on-device and mobile designed models first launched with Gemma 3n.

The bundle includes four models, including Gemma's first MoE model, which can all fit on a single NVIDIA H100 GPU and supports over 140 languages.

Modern physical AI agents are evolving rapidly with Gemma 4 models that integrate audio, multimodal perception, and deep reasoning capabilities.

The 31B and 26B A4B variants are high-performing reasoning models suitable for both local and data center environments.

NVFP4 enables 4-bit precision while maintaining nearly identical accuracy to 8-bit precision, increasing performance per watt and lowering cost per token.

By using SAM, the Alta team has been able to process more than 20 million images without incurring exorbitant costs, allowing them to focus on building the best possible product for their users.

If we knew that every image uploaded was a beautiful model shot, segmentation would be far easier, but because of the nature of user-uploaded content, we need the best possible segmentation.

Fashion in particular has one of the most complex image datasets, especially because of the inconsistent nature of user-uploaded content.

Built from the same world-class research and technology as Gemini 3

Engineered from the ground up for maximum compute and memory efficiency

The edge models feature a 128K context window, while the larger models offer up to 256K

Byte for byte, the most capable open models

Within ChatGPT Business and Enterprise, the number of Codex users has grown 6x since January.

We are especially interested in work that is empirically grounded, technically strong, and relevant to the broader research community.

The vast majority of the new compute will be sited in the United States, making this partnership a major expansion of our November 2025 commitment to invest $50 billion in strengthening American computing infrastructure.

Claude remains the only frontier AI model available to customers on all three of the world's largest cloud platforms: Amazon Web Services (Bedrock), Google Cloud (Vertex AI), and Microsoft Azure (Foundry).

We train and run Claude on a range of AI hardware—AWS Trainium, Google TPUs, and NVIDIA GPUs—which means we can match workloads to the chips best suited for them.

Demand from Claude customers has accelerated in 2026. Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025.

abiding

occasional ladies days

filling the stand

women's leagues

catchy

In most other societies,an admission of human err o r m i g h t s e e m c o m m o nplace. But not in the SovietUnion, where for decades official failures have seldombeen acknowledged, officialsins seldom recognized. Disasters such as plane crashesa n d e a r t h q u a k e s a r e l i k etrees falling in the forest when no one ispresent. No one ever hears the crash

mic-power facility, Soviet officialsused the accident report as a platform fortheir campaign against the American nuclear-defense program. After first ignoring and then minimizing the mishap,Moscow has tried to establish a link between Chernobyl and atomic weapons.Said the report: "The accident at theChernobyl nuclear-power plant has againdemonstrated the danger of uncontrollednuclear power and highlighted the destructive consequences to which its military use or damage to peaceful nuclear facilities during military operations couldlead." And Petrosyants told the press conference, "The explosion of the smallestnuclear warhead would be equal to threeChernobyls." U.S. officials quickly pointed out that Moscow's attempt to linkChernobyl to the arms race was a predictable effort to divert attention from itsown failures.Indeed,

So far, 31 people whowere in or near the plant atthe time of the accident havedied, and that number onlybegins to state the extent ofthe health damage. Usingdata from the report on thelevels of human contamination, American experts conclude that a total of morethan 5,000 people are likelyt o d i e p r e m a t u r e l y f r o mradiation-induced cancer.There will be 10,000 cases ofthyroid cancer alone, the experts predict, resulting in1,500 deaths. Though there is still concernabout contamination in other Europeancountries, the information indicates thatall the premature deaths will be in the Soviet Union

Most linguists and educators agree that AI has transformed language learning by offering personalized, optimized tools (Rusmiyanto et al., 2023).AI enables adaptive, inclusive, and engaging education (Luckin et al., 2016), making learning more dynamic and assistive (Schmidt & Strasser, 2018). Other benefits of AI in language teaching include the possibility of: personalized and adaptive learning (Titova, 2024; Tolstyh, 2023), real-time feedback and assessment (Sysoev et al., 2024), gamification and immersive experiences (Celik et al., 2022), overcoming psychological barriers (Chen et al., 2020),access to diverse resources (Berendt et al., 2020; Karatas et al., 2024), 24/7 availability and sense of community (Strasser, 2021).

Most theoretical researchers and practitioners believe AI is displacing traditional teaching methods and reshaping the teacher's role. AI technologies are undoubtedly an achievement and a step forward. However, practical experience in teaching foreign languages within non-linguistic state education systems suggests the opposite. At this stage of methodological development, use of AI by students doesn’t only fail to accelerate learning process but, on the contrary, complicates and slows it down. Improper use of AI prevents students from fully mastering and practicing the material.

However, the use of technology is not a goal in itself but a tool for fulfilling the knowledge needs of all the participants of the educational process.

Now it’s your turn, choose some data that you might want to store on a social media type, and think through the storage types and constraints you might want to use:

Metadata is information about some data. So we often think about a dataset as consisting of the main pieces of data (whatever those are in a specific situation), and whatever other information we have about that data (metadata). For example: If we think of a tweet’s contents (text and photos) as the main data of a tweet, then additional information such as the user, time, and responses would be considered metadata. If we download information about a set of tweets (text, user, time, etc.) to analyze later, we might consider that set of information as the main data, and our metadata might be information about our download process, such as when we collected the tweet information, which search term we used to find it, etc.

Metadata is information about some data. So we often think about a dataset as consisting of the main pieces of data (whatever those are in a specific situation), and whatever other information we have about that data (metadata).

A scaffold is not meant to be a permanent support. Rather, the ultimate goal of scaffolded instruction is to facilitate a student in completing the same type of task independently in the future once they have acquired the necessary language and/or skill. As our colleague Tonya Ward Singer instructs, you need to both “use and lose” scaffolds (2018).

Success folders are compilations of student work. Each week or every two weeks, you can return student work and ask your students to select a piece of work that they are most proud of or that they worked hard to complete. When you are setting up this procedure, be sure to model the process for students and talk about what work they might want to include as an example. You can have students write a short explanation of why they included a particular piece of work on an index card that you attach to the piece of work. These success folders can be a great resource for parent or guardian conferences and student goal-setting meetings. You can also have students decorate their folders with inspirational quotes or images that represent success for them.

As teachers, we can model what it means to have an academic mindset by describing our own challenges and how we work to overcome them. We can acknowledge that challenge and failure are both a normal and temporary part of the learning process. It is critical for us as educators to express our vulnerabilities so that students know it’s OK to feel the same way. We can also set up procedures where students can model their problem-solving strategies and academic mindsets for other students. It is important to consider how you can provide an opportunity for students to model their own academic mindset throughout the year.

Just as we want educators to view MLs from an assets-based perspective in which they see and value what each student brings to their learning, we also want MLs to see in themselves someone who can be a successful learner. Farrington (2013) describes four characteristics of an academic mindset: I belong here.I can succeed at this.My ability and competence grow with my effort.This work has value for me.

What does this bot do that a normal person wouldn’t be able to, or wouldn’t be able to as easily? Who is in charge of creating and running this bot? Does the fact that it is a bot change how you feel about its actions?

umbrella term

Crafting truly authoritative texts requires more than just presenting data; it involves skillfully integrating evidence, acknowledging diverse perspectives, and demonstrating a nuanced understanding of the subject matter

How often do you hear phrases like “social media isn’t real life”? How do you think about the relationship between social media and “real life”?

Pluto caught sight of her                                   620 and, in almost the same instant, loved her and carried her away—that’s how rapid love can be.

As he moved around, Venus of Eryx sitting on her mountain, noticed Pluto.(21) Embracing her winged son, she said:                                                            ‘Cupid, my son, my weapon, my hands, my power— take those arrows you use to overwhelm                                 580 all beings, and shoot your swift-flying dart into the heart of Pluto, whose lot won the last of the three kingdoms.

A good way to start is to make a list of keywords, known authors, organizations, and previously identified sources related to your topic. For example, imagine you have been assigned an argument project and chosen artificial intelligence as the topic.

Ce phénomène, que les chercheurs ont appelé « The Oprah Effect », ne doit pas être vu comme une simple curiosité médiatique ou une anecdote liée à la culture populaire.

First, I must confessthat over the past few years I have been gravely disappointed with the white moderate. I have almostreached the regrettable conclusion that the Negro’s great stumbling block in his stride towardfreedom is not the White Citizen’s Counciler or the Ku Klux Klanner, but the white moderate, who ismore devoted to “order” than to justice

One who breaks anunjust law must do so openly, lovingly, and with a willingness to accept the penalty

« How the ’Clean Girl Aesthetic’ is Taking Over TikTok ». s. d. Consulté le 28 mars 2026. https://www.skysociety.co/blog/how-the-clean-girl-aesthetic-is-taking-over-tiktok. « La Distinction ». 2026. Wikipédia. https://fr.wikipedia.org/w/index.php?title=La_Distinction&oldid=233695165. Marain, Alexandre. 2025. « Exit la “clean girl”, place à la “messy girl” ! » https://www.vogue.fr/article/tendance-mode-messy-girl-2025-phenomene. « Un instant… ». s. d. Consulté le 28 mars 2026.

you might notice more people who are older than the more traditional-aged college students

It has been made into the confused, the trivial, the psychotic, the plasticized sensation

the context of male models of power.

phosphorus cannot be manufactured out of thin air. Its supply is limited to sources that can be mined in deposits that are unevenly spread around the Earth’s surface, and there are fears that its production is not only limited but may have peaked and will begin declining by 2030, although that scenario is highly debated.131

This has resulted in a substantial and largely irreversible loss in the diversity of life on Earth.”130

we have destroyed 83 percent of wild anima

a gamble that the fossil-fuel-consuming,

No matter when you compose the conclusion, it should sum up your main ideas and revisit your thesis.

Students were divided on the value of GenAI for synthesizing conclusions, acknowledging its capacity to generate new perspectives while critiquing its tendency to oversimplify and produce biased outputs [3,11].

Conversely, students with higher ratings, while acknowledging GenAI’s limitations and the need for critical evaluation, appreciated its capacity to suggest novel perspectives that could serve as starting points for more detailed analysis and development (“I only used it as a base to write my own conclusion,” (Chemistry), “Useful to check that what I concluded was related to my objectives and hypotheses,” (Business Administration), and “To explain results I wasn’t expecting, and, in general, to explain what I obtained,” (Psychology)).

Chan and Hu [8] observed that student reliance on ChatGPT intensifies during the preliminary stages of academic work—such as brainstorming or outlining—when immediate feedback can jump-start the creative process.

In contrast, other studies suggest that ChatGPT can function as a motivational scaffold, enhancing creative confidence and learner autonomy [26]. This dichotomy highlights the nuanced role that generative AI plays in learning.

reviewers can sometimes accurately guess the identity of an author.

Providing clear DEI statements

nterpreted as issues of bias was an emphasis on being constructive

A sample review specific to science education is included as a resource for reviewers. JRST (2021) also includesa DEI statement:

As with all other journals,formatting expectations and the logistics of the submission process are also well‐described.

DISCUSSION

eaving the authors'experiences

female faculty in these positions report spending more time onteaching than research in a typical week whereas men report more time doing research than teaching (Rissleret al.,2020), men being more likely to proactively negotiate with coauthors for the prestigious authorship position(West et al.,2013), bias in peer review toward manuscripts where men occupy the prestigious authorship position

PUBLISHING AND THE PEER‐REVIEW PROCESS

The relationship between faculty and students is like the relationship between a river and its water: In the short term, the river tells the water where to go, but in the long term, the water tells the river where to go.

Q2 2026

AI elevates people; it does not substitute them.

“Ganas. That’s all you need. The desire to learn.”

Code viewof reset_adoption = Inputs.button("Reset adoption defaults", { reduce: () => { // Set viewof values back to defaults viewof p_hydro.value = 0.75; viewof p_hydro.dispatchEvent(new Event("input", {bubbles: true})); viewof p_foodgrade.value = 0.65; viewof p_foodgrade.dispatchEvent(new Event("input", {bubbles: true})); viewof p_recfactors.value = 0.5; viewof p_recfactors.dispatchEvent(new Event("input", {bubbles: true})); viewof gf_progress.value = 50; viewof gf_progress.dispatchEvent(new Event("input", {bubbles: true})); } }) Reset adoption defaultsreset_adoption = 0 Code viewof p_hydro = Inputs.range([0.3, 0.95], { value: 0.75, step: 0.05, label: "P(Hydrolysates for basal media)" })

Their soulless, anticivic,and anticommunity designs are putatively fostering an alienation thatthreatens the fabric of American social life

In this view, the emptiness ofLittleton’s soul was caused by the emptiness of its landscape

grade CAPEX 5–25/kgcapacity|Basecapitalcostat20kTA|CAPEX_{}$ in scaling equation

effort to secure the world’s most critical software.