Bibliographic record
Abstract
ABSTRACT: We develop a directed search model of relationship formation which can disentangle male and female preferences for types of partners and for different relationship terms using only a cross-section of observed matches. Individuals direct their search to a particular type of match on the basis of (i) the terms of the relationship, (ii) the type of partner, and (iii) the endogenously determined probability of matching. If men outnumber women, they tend to trade a low probability of a preferred match for a high probability of a less-preferred match; the analogous statement holds for women. Using data from National Longitudinal Study of Adolescent Health we estimate the equilibrium matching model with high school relationships. Variation in gender ratios is used to uncover male and female preferences. Estimates from the structural model match subjective data on whether sex would occur in one’s ideal relationship. The equilibrium result shows that some women would ideally not have sex, but do so out of matching concerns; the reverse is true for men. We thank Aloysius Siow as well as seminar participants at Boston College, Calgary, Georgetown, Pittsburgh and Princeton. This paper uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".