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 type i men to unmarried type j women 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".