Evidence that gendered wording in job advertisements exists and sustains gender inequality.
Why this work is in the frame
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Bibliographic record
Abstract
Social dominance theory (Sidanius & Pratto, 1999) contends that institutional-level mechanisms exist that reinforce and perpetuate existing group-based inequalities, but very few such mechanisms have been empirically demonstrated. We propose that gendered wording (i.e., masculine- and feminine-themed words, such as those associated with gender stereotypes) may be a heretofore unacknowledged, institutional-level mechanism of inequality maintenance. Employing both archival and experimental analyses, the present research demonstrates that gendered wording commonly employed in job recruitment materials can maintain gender inequality in traditionally male-dominated occupations. Studies 1 and 2 demonstrated the existence of subtle but systematic wording differences within a randomly sampled set of job advertisements. Results indicated that job advertisements for male-dominated areas employed greater masculine wording (i.e., words associated with male stereotypes, such as leader, competitive, dominant) than advertisements within female-dominated areas. No difference in the presence of feminine wording (i.e., words associated with female stereotypes, such as support, understand, interpersonal) emerged across male- and female-dominated areas. Next, the consequences of highly masculine wording were tested across 3 experimental studies. When job advertisements were constructed to include more masculine than feminine wording, participants perceived more men within these occupations (Study 3), and importantly, women found these jobs less appealing (Studies 4 and 5). Results confirmed that perceptions of belongingness (but not perceived skills) mediated the effect of gendered wording on job appeal (Study 5). The function of gendered wording in maintaining traditional gender divisions, implications for gender parity, and theoretical models of inequality are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it