Intrasexual Competition and Mothers: Perceptions of Those Who Self-promote and Derogate Their Rivals
Bibliographic record
Abstract
It has been previously demonstrated that women who utilize the competitor derogation strategy (which requires fierce and explicit tactics to secure resources) are perceived more negatively than those who utilize the self-promoting strategy (which includes subtler tactics to secure resources).Some of these resources are directly related to and for the benefit of a woman's offspring.However, it remains unknown how women who use these strategies for accessing resources for their offspring are perceived by potential rivals (other females) and potential mates (males).We propose that mothers who derogate their competition (other mothers) will be seen more negatively than those who self-promote.Using a pre-post study design, female participants rated 12 mothers' photographs for attractiveness, competency as a mother, and personality.In the pre-condition participants rated the woman in the photograph, while in the post-condition the participants rated her after being told the woman made a 'Facebook post' containing maternal competitor derogation or self-promotion.Differences in pre-post ratings were calculated, with change presumably caused by strategy use.Results indicate women who promote their maternal competency via self-promotion are perceived to be less likeable compared to baseline ratings, and women who derogate their competition are perceived less positively on the majority of attributes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".