Institutional signals of inclusion: Increasing perceptions of possibilities available for the self and others in STEM
Bibliographic record
Abstract
Women in Science, Technology, Engineering, and Math (STEM) face systemic barriers due to the prominent masculine culture that has been established within the field. The present research aims to examine strategies for improving the experiences of women in STEM by exploring the benefits that institutional signals of inclusion can have on perceptions of what is possible for the self and others at work. Across four studies, participants were randomly assigned to one of two conditions where we manipulated the extent to which the company policies at a fictitious technology development company were gender-inclusive. Studies 1 through 3 assessed the impact of gender-inclusive policies on beliefs regarding how possible the work culture of the described organization would make it to behave inclusively (Study 1), be your authentic self (Study 2), and achieve professional goals (Study 3). Results revealed that gender-inclusive policies led individuals to anticipate a warmer interpersonal climate and possess a stronger belief that it would be possible to behave in an inclusive manner, authentically express themselves, and achieve professional goals. In Study 4, participants rated their preferences between job candidates and selected who they would hire for a position in STEM from an array of candidate profiles. The findings demonstrated that gender-inclusive policies result in a significant preference for qualified women candidates and increase the likelihood of hiring qualified women in STEM. This research suggests strategies to improve experiences in STEM by expanding perceptions of what is possible for the self and others in male-dominated domains.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".