Bibliographic record
Abstract
Although behavioural economists are not rhetoricians, and rhetoricians are not behavioural economists, they are both interested in persuasion, even as they come at it from different points of view. Lecture 10 argues that behavioural economics examines our choice-making practices and considers how a range of influences works in concert with conventional economic interests to shape the procedures by which we come to decisions. These influences use rhetoric to nudge people to adopt particular beliefs, engage in specific behaviour, and endorse ideas believed to be in the public interest. Les économistes comportementaux ne sont pas rhétoriciens, et les rhétoriciens ne sont pas économistes comportementaux, mais ils s’intéressent tous les deux à la persuasion, même si leurs points de vue diffèrent. Le cours 10 soutient que l’économie comportementale examine notre manière de faire des choix et il considère comment un éventail d’influences, de concert avec des intérêts économiques conventionnels, façonne les procédures par lesquelles on prend des décisions. Ces influences utilisent la rhétorique afin d’inciter les gens à adopter des croyances particulières, adopter des comportements spécifiques, et appuyer des idées censées être dans l’intérêt public.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".