Environmental influences on human mate preference across Canada
Bibliographic record
Abstract
Sexual selection and mate preference is dynamic and can be influenced by a number of environmental factors. The purpose of my study was to determine if there is a correlation between environmental factors and human female mate preference in cities across Canada. Environmental, economic, and mate preference data were collected from 26 cities across Canada. Mate preference data was collected by looking at the first 50 online profiles for each city on a popular online dating site. Across the 26 cities, I recorded variation in both environmental (e.g., sex ratio, population density, population size) and mate preference data (resource holding potential, physical attractiveness, emotional appeal and personal activities and interests). I then asked whether the observed variation in stated preferences could be explained by variation in environmental and economic conditions across these cities. Furthermore, because this dating site includes the poster's characteristics, I also examined whether age was correlated to mate preference and if it explained the relationships between mate preference and the environmental influences. I found that preference for resource holding potential was positively related to population density and negatively related to age. In addition, I found that population size was positively related to preference for physical attractiveness and negatively related to preference for personal activities and interests. The findings from this research expand our current knowledge of the influences of environment on female human mate preference. My work also highlights the importance of examining poster characteristics, especially age, as they tend to vary across cities and can have a strong effect on observed patterns of female human mate preferences.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".