Dominance is necessary to explain human status hierarchies
Bibliographic record
Abstract
Durkee et al. (2020) conducted a cross-cultural investigation of people’s beliefs about how traits, behaviors, and practices that enhance an individual’s perceived ability to generate benefits (prestige) or inflict costs (dominance) promote perceived social status in humans. In this letter (also see online extended version), we (a) identify multicollinearity in the authors’ statistical analyses and explain how this statistical problem renders their results inconclusive as to how benefit-delivery and cost-infliction contribute to status allocation; (b) outline flaws in the authors’ operationalization and measures of social status, and discuss how they bias results toward benefit-delivery and underestimate any effect of cost-infliction; and (c) discuss a broader problem with the critical assumption underlying Durkee et al.’s approach: people’s subjective beliefs about what determines status do not serve as sufficient evidence for determining how status asymmetries are actually established in real life. Together, these three major issues severely undermine the authors’ conclusion that there is little evidence for dominance. In closing, we briefly survey the broader empirical record on actual status relations among real people (rather than people’s beliefs about what leads to status), conducted both in the lab and in naturalistic settings; these studies consistently yield opposite conclusions to Durkee et al. and demonstrate that both prestige and dominance govern human status hierarchies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".