Homo infidelis: preferências, assimetria de informação e signaling
Bibliographic record
Abstract
In this reseach we analyze the efficiency of signaling strategies in the context of extra-dyadic relationships. Aditionally, we measure both the declared preferences and revealed preferenced by individuals’ interactions in the virtual environment, comparing genders and countries. Previous researches about the extra-dyadic subject were based on methodologies with substancial gaps: low number of observations, samples restricted to college students, self-declared bias jeopardizing the precise interpretation of reality, and the absense of international comparisons. Suchs flaws were overcomed by this study. We analysed 100 thousand individuals, being 6 thousand men and 4 thousand women for each of the 10 countries of our sample: Argentina, Australia, Brazil, Canada, Chile, Italy, Mexico, Spain, UK and USA. Therefore, we have a vast sample in terms os nations and number of observations. Empirical evidences here revealed show that men are more active in the extramarital conquest, rebutting the suggestion of an eventual behavioral convergence between genders. Men contacted an average of 30 different women during the time window of 3 months, against 13 in the opposite direction. In terms of physical preferences, we verified that individuals from both genders look for an idealized partner to consummate the relationship. Women show interest in tall and experienced men. Men desire good shaped and younger women. Shorter and younger men, as well as older and taller women, were the most rejected individuals from the sample. In relation to the declared and revealed preferences about the type of desired extra-dyadic encounter, both men and women showed more interest in a fast affair, indicating the will in a shallow, casual and physical-sexual contact only, with low emotional bonds. The least chosen option was a 'virtual affair', suggesting the effective intention to materialize the infidelity, and not to keep it restrict to the virtual environment. Finally, we verified that the analyzed signaling stratetegies (through photos, feedback and virtual gifts) were strongly effective. In all cases individuals that adopted such tactics achieved a substantial competitive advantage in relation to those who didn’t, and due to that were endowed with greater attractiveness, receiving a high portion of messages and contacts.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".