Bibliographic record
Abstract
Inductive generalization (i.e., validation of general claims based on empirical evidence) is a critical method in science that is also notoriously difficult to justify. In particular, induction is not required to test honestly produced claims. We examine the role of induction in an economic model where agents may strategically misrepresent what they know. Our main result shows that induction is required to test and potentially reject expert’s claims. This result provides an economic argument for induction based on incentive problems. ∗We thank Wojciech Olszewski, Eran Shmaya, Marciano Siniscalchi and Rakesh Vohra for some useful discussions, as well as seminar audiences at the Canadian Economic Theory Conference 2012, the Fifth Transatlantic Theory Workshop, the Summer meeting of the Econometric Society 2012, XIII Latin American Workshop in Economic Theory, Jolate conference in Bogota and the Washington University seminar series. Sandroni gratefully acknowledges financial support from the National Science Foundation. All errors are ours. †Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, Evanston, IL 60208. (e-mail: al-najjar@kellogg.northwestern.edu) ‡Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, Evanston, IL 60208. (e-mail: l-pomatto@kellogg.northwestern.edu) §Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, Evanston, IL 60208 (e-mail: sandroni@kellogg.northwestern.edu).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".