Bibliographic record
Abstract
After years of treacherous efforts to break out of traditional gender roles, one seems to remain across different ages, education levels, and income groups. 82% of men and 58% of women, agree that men should pay for common expenses. Additionally, two-fifths of women reported being bothered if the man didn’t offer to pick up the tab. If we expect gender equality in all other aspects of society, how come “dating” is immune to the concept? In this study, the underlying preferences for partner selection are explored, exploring the evolutionary drive to find a mate who is financially stable and can provide security for a future offspring. Sexual selection has evolved over generations and led to psychological mechanisms used to assess the viability of a mate in order to increase reproductive success. This study shows that, ancestral women evolved to select men that show signs of power, status and stability to ensure that he can provide for their offspring in the future; what to cavewomen was prey from a hunt, to the modern woman is a credit card. While this might be considered a regression of equality, the evolutionary psychology of mating selection can explain how this display of economic stability and power, attracts the female. Thanks to ancestral women’s mating choices and sexual selection, it seems as though chivalry is not dead, after all.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".