Bibliographic record
Abstract
Abstract. The paper surveys the Choo and Siow (2006a; CS) marriage matching model and its extensions. CS derives a behavioural marriage‐matching function. The collective model of intra‐household allocations can be integrated into this framework. Spousal labour supplies respond to changing marriage market conditions. Marriage market tightness, the ratio of unmarried typeimen to unmarried typejwomen is a sufficient statistic for marriage market conditions for those types of individuals. The hypothesis that spousal labour supplies vary to equilibrate the marriage market has overidentifying restrictions. The framework extends to a dynamic marriage‐matching environment. Empirically, this paper shows how the famine caused by the great leap forward in Sichuan affected the marital behaviour of famine‐born cohorts. Marriage market tightness is shown to be a useful statistic for summarizing marriage market conditions in the United States. Marriage market conditions in the contemporary United States primarily affect spousal labour force participation rather than hours of work.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 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".