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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".