Measurement Issues in Tests of the Socioecological Complexity Hypothesis
Bibliographic record
Abstract
Recent research has advanced a socioecological theory to account for differences in the strengths of covariances among disparate personality measurements in different cultures. According to this socioecological complexity hypothesis, niche diversity is greater in more complex societies and this relaxes the covariances among personality traits (e.g., see Lukaszewski et al., 2017). While the socioecological complexity hypothesis is novel and interesting, we suggest that approaches used to test it thus far are conceptually and methodologically flawed. Accordingly, extant findings should be considered cautiously and not construed as evidence against alternative explanations for differences in personality or other behavioral trait covariances within or across countries. To advance the literature, here we review measurement issues that require attention in efforts to test the socioecological complexity hypothesis and then describe approaches that may aide researchers in overcoming them.
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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 | 0.318 | 0.705 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| 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".