Bibliographic record
Abstract
In the last quarter of the 20th century, the discipline of economics suffered a profound change when psychologists intervened in microeconomics. Classically, the agent of economic decisions was considered purely rational and cold-blooded, with the single goal of maximizing utility. Researchers like Kahneman, Tversky, Thaler, and Ariely experimentally showed that such an agent does not exist in real life: they are 'econs' instead of humans. This was the dawn of behavioral economics, an exciting field that exposed the biases and limitations of humans when making decisions. In this short course, we will outline behavioral economics' history and main findings, posing the provocative question of whether logic could also benefit from assuming that logical reasoning is not carried out by perfectly rational agents in real life but by mere humans. Is it time for the birth of "behavioral logic"? The second session of the course will focus on the process of developing tools to assess human behavior. At this point, mathematics and statistics enter a fruitful dialogue with psychology, aimed at designing instruments that scientifically measure a behavioral characteristic (namely construct) with the highest accuracy and validity. Overall, the main goal of the course is to show experts in logic the potentialities of psychology to assess logical reasoning in actual human beings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.609 | 0.423 |
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".