Greater precision, not parsimony, is the key to testing the peri-ovulation spandrel hypothesis: a response to comments on Havliček et al. 2015
Bibliographic record
Abstract
We welcome the wide range of comments provoked by the introduction of our alternative theoretical perspective on the peri-ovulation paradigm (Havliček et al. 2015)—some positive and some very critical—and here we address briefly some of the key objections. First, a key assumption of our “peri-ovulation spandrel” hypothesis is that the formation of long-term relationships is critical to understanding human mate preferences. Echoing Dixson (2015), we are skeptical about the ecological validity of distinguishing between short-term and long-term mating preferences. Researchers frequently ask participants to describe their preferences in each context and, as Haselton (2015) describes, effects are often stronger in short-term contexts. In reality, little is known about how these categories are interpreted and distinguished by participants. Moreover, if such a distinction does exist, the extent to which meaningful change in mating strategy can be elicited by brief instructions on a questionnaire is likely to be, at best, individually variable. We suspect that many participants, especially in non-western communities, do not easily conceptualize the distinction, and its validity should be theoretically and methodologically reexamined and validated before robust claims are made about its utility.
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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 | 0.091 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.040 | 0.079 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".