Validating the semantic misattribution procedure as an implicit measure of gender stereotyping
Bibliographic record
Abstract
Abstract The current research tested the validity of the semantic misattribution procedure (SMP)—a variant of the affect misattribution procedure—as an implicit measure of gender stereotyping. In three studies (N = 604), prime words of gender‐stereotypical occupations (e.g., nurse, doctor) influenced participants' guesses of whether unknown Chinese ideographs referred to male or female names in a stereotype‐congruent manner. Priming scores of gender stereotyping showed high internal consistency and construct‐valid correlations with explicit measures of sexism. Discriminant validity of gender stereotyping scores was tested by investigating relations with priming effects involving grammatical gender (e.g., mother, father). Evidence for discriminant validity was obtained when (1) trials from the two priming measures were presented in a blocked rather than interspersed manner and (2) the measure of stereotypical gender priming preceded the measure of grammatical gender priming. Overall, the SMP showed good psychometric properties and construct validity for the assessment of gender stereotyping.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".