Mate choice copying and mate quality bias: are they different processes?
Bibliographic record
Abstract
Mate preferences can be highly variable within populations (Andersson 1994; Jennions and Petrie 1997). Part of this variation is owing to flexibility in mating preferences expressed by individuals during their lifetime (Jennions and Petrie 1997). Of increasing interest over the past 2 decades has been the potential role of individual social experiences underlying such flexibility in mate choice decisions (e.g., Gibson and Höglund 1992; Dugatkin 1996; Witte 2006). It is now well established that individuals can acquire social information from conspecifics about potential mates, which can subsequently influence their choice of mates and lead to nonindependent mate choice (e.g., Pruett-Jones 1992; Dugatkin 1996; Westneat et al. 2000; Witte 2006). One form of nonindependent choice is mate choice copying (Wade and Pruett-Jones 1990; Dugatkin 1992, 1996; Gibson and Höglund 1992; Pruett-Jones 1992; Westneat et al. 2000; Witte 2006). Although there has been some disagreement and confusion over which phenomena or processes constitute mate choice copying, it is now generally accepted that mate choice copying is operationally a form of nonindependent mate choice resulting from social learning, in which an individual gains social information about potential mates by observing sexual interactions between nearby male and female conspecifics and uses this learned-association information later in choosing a mate. Mate choice copying is considered to have occurred if a focal individual's observation of a sexual interaction between a male and a female increases its likelihood of subsequently preferring or rejecting the individual observed mating (Pruett-Jones 1992; Dugatkin 1996; Kraak 1996; Westneat et al. 2000; Witte 2006). Mate choice copying can be exhibited by females and males, but it appears to be more prevalent in females (Dugatkin 1996; Westneat et al. 2000; Witte 2006). Therefore, we restrict our consideration here to mate choice copying by females.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".