Bibliographic record
Abstract
We suggest a family bargaining model where human capital investment decisions are made non‐cooperatively in a first stage, while day‐to‐day allocation of time is determined later through Nash bargaining, but with non‐cooperative behaviour as the fall‐back. One finding is that overinvestment in education may be even more of a problem in such a semi‐cooperative model than in a fully non‐cooperative one. Even though both the semi‐cooperative model and the fully non‐cooperative model predict overinvestment in education, policy conclusions that follow from the two models are distinctly different. JEL Classification: D13, J24 Les auteurs suggèrent un modèle de famille qui marchande où les décisions d'investissement en capital humain sont prises de manière non‐coopérative dans un premier temps, alors que l'allocation du temps au jour le jour est déterminée plus tard par un marchandage à la Nash, mais avec un comportement noncoopératif comme choix de second ordre. On découvre que le surinvestissement dans l'éducation peut être encore plus problématique dans un tel modèle de semi‐coopération que dans un modèle de non coopération. Même si les deux modèles prévoient un surinves;chtissement dans l'éducation, les conclusions au plan de la politique publique qui découlent de ces deux modèles sont fort différentes.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".