The pleasures and pitfalls of studying humans from a behavioral ecological perspective
Bibliographic record
Abstract
We know it’s incredibly tedious, but we have to admit we agree with everything Nettle et al. (2013) say. Not only is that tedious, it also makes for a rather brief commentary. So, in an effort to keep the conversation lively, we would like to address some additional issues that highlight the pleasures and pitfalls of studying humans from a behavioral ecological perspective. Before we begin in earnest, we think it is perhaps worth drawing a distinction between the contribution made by human behavioral ecology (HBE) to the broader field of behavioral ecology (BE) versus the contribution that BE makes to the study of humans from an evolutionary perspective. We think this is a distinction worth making as Nettle et al. (2013) interpret the low number of papers published in flagship BE journals as a signal of a (potentially increasing) risk of isolation from the broader field; something they suggest can be traced, at least partly, to the “disco problem” as defined by West et al. (2011). Although this may well be true, it is also worth considering whether these numbers are, in fact, roughly what we’d expect for such a large, long-lived mammal (other long-lived species like elephants and chimpanzees are similarly underrepresented compared with birds, fish, and insects). We are, after all, a terrible species in which to address fundamental evolutionary questions, not only because of our long life spans and slow rates of reproduction but also because of the obvious ethical constraints placed on experimental studies of human behavior. It is a rather sobering conclusion, but if we take a broader, less anthropocentric view, it may be that we cannot, or rather should not, expect HBE to make major theoretical or empirical contributions to BE, which can apply to the field as a whole. This shouldn’t be confused, of course, with our saying that it is not worthwhile to study humans or indeed other long-lived mammals (we’d both be out of a job for a start, if this were the case).
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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 | 0.059 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.013 | 0.033 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".