Bibliographic record
Abstract
Artificial Intelligence and Education in our TimeThere is presently a lot of excitement around the importance of educating ourselves at all levels about the uses and abuses of Artificial Intelligence (AI).Research applications to funding agencies abound that wish to show to students of different ages and abilities how to access some of the most recent general AI programs such as ChatGPT and their equivalents for purposes their academic work.Among the commonest is aid in writing scholarly essays.In this part of the world, essays in English are paramount.But any language will do these days.Certainly, it is important that many who need it can get help in producing a written piece of a topic of one's choice.But of course, the other side of this is that such written pieces can be produced as one's own work for credit in some educational arrangement or another at school, college, or university level.So, there is equal interest in teaching learners at all levels the moral wrongness of how one might be offering work as one's own that is really just the product of access to these generals AI programs like ChatGPT.It seems to me that teaching how to properly use such AI systems for particular purposes is a valuable thing.And teaching the ethics of their use is another valuable thing.But it also seems to me that the most valuable thing that we might each learn, in an era of omnipresent AI, is what thinking is necessary in order to create a new and useful piece of AI that can perform something valuable that some of us can do or perform that could be turned into a system that could be turned into AI for the benefit of others too.That is learning ourselves how to break down the steps we actually use to accomplish an important task that we can do and other find difficult.When one understood these important steps, the steps that we uniquely easily and automatically perform , could be used to create new and useful artificial intelligence.One can illustrate what I mean by a problem that occurred to historians in the 1970's who were interested in the history of populations and social structure.In order to look at whole populations, historians had discovered that by the mid-19th century large files of entire populations were being routinely generated.An obvious one was census of entire populations in many countries.The Bible tells us that the Romans took such censuses of everybody in their empire at the time of Jesus's birth.And most European countries and perhaps China took such censuses routinely inte-mid 19th century.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".