Content analysis of online personal advertisements : attributes desired and offered
Bibliographic record
Abstract
The evolutionary perspective has often been used in the study of mate preferences. Guided by an evolutionary-based theory called Sexual Strategies Theory (Buss, 1994), the current research examined the effect of the type of relationship sought, represented by an intimate encounter, a date, and a relationship, on attributes desired and offered by online dating ad placers. Online personal advertisements (N = 120) from a Canadian dating web site were content analyzed for the following attributes: physical attractiveness, resources, commitment, social skills, social attitudes, and interests. Attributes desired and offered did not differ by gender but did differ by type of relationship. Ad placers desired more commitment, social skills, and social attitudes when seeking a relationship than an intimate encounter. These attributes, in addition to resources, were offered more when seeking a relationship than an intimate encounter. Contrary to the theory, physical attractiveness was not desired or offered more when seeking an intimate encounter and to date compared to a relationship. Gender moderated the relationship between type of relationship sought and resources offered but none of the other attributes. A shift away from gender differences in mate preferences is suggested. The limitations of the theory for explaining the results are discussed and alternative explanations are provided.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".