How EIRD Is Sex Research?: A Commentary and Reanalysis of Klein et al. (2021)
Bibliographic record
Abstract
Klein, Savaș, and Conley (2021) argued that sexual science is overdependent on WEIRD (Western, Educated, Industrialized, Rich, and Democratic) samples. Though we agree that sexual science needs to increase its generalizability and inclusivity, we describe concerns with their measurement strategy of categorizing samples as WEIRD or Not WEIRD based on the country from which a sample was drawn. Reanalyzing their data with publicly available global metrics of Education, Industrialization, Richness, and Democratic Values (what we refer to as EIRDness), we find (1) EIRDness metrics were not particularly correlated; (2) countries coded as WEIRD by Klein et al. do not appear reliably EIRDer than those that were not; and (3) and categorical measurement models of EIRDness did not support profiles of EIRD and Not EIRD countries. With these limitations in mind, we then express further concerns about the application utility of Klein et al.'s WEIRDness critique, and unintended political implications embedded in its methodology. We conclude by harkening back to critiques of the WEIRD framework, and suggest that the pursuit of a more equitable and just sexual science - which we applaud Klein et al. for pushing our field to consider - may be better served to alternative frameworks for critiquing its sampling practices.
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 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 | 0.085 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.042 | 0.065 |
| Insufficient payload (model declined to judge) | 0.003 | 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".