Viewpoint: On the Generalizability of Lab Behaviour to the Field.” Canadian
Bibliographic record
Abstract
Abstract. We can think of no question more fundamental to experimental economics than understanding whether, and under what circumstances, laboratory results generalize to naturally occurring environments. In this paper, we extend Levitt and List (2006) to the class of games in which financial payoffs and ‘doing the right thing ’ are not necessarily in conflict. We argue that behaviour is crucially linked to not only the preferences of people, but also the properties of the situation. By doing so, we are able to provide a road map of the psychological and economic properties of people and situations that might interfere with generalizability of laboratory result from a broad class of games. JEL classification: C9 A propos de la possibilitédegénéraliser les comportements de laboratoire à ce qui se passe sur le terrain. Il n’y a pas de question plus fondamentale en économie expérimentale que de savoir si et en quelles circonstances les résultats de laboratoire peuvent être généralisés àce qui se passe sur le terrain. Dans ce texte, on développe les résultats de Levitt et List (2006) pour les appliquer à une classe de jeux dans lesquels les résultats financiers et ≪faire la bonne chose ≫ ne sont pas nécessairement des choix conflictuels. Le comportement n’est pas une simple question de préférences des gens mais aussi un écho des propriétés de Both authors are members of NBER. Thanks to seminar participants at the 2005 International Meetings of the ESA for useful suggestions. Excellent suggestions from James Andreoni,
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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 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".