Gender-Inclusive Practices in Campus Women’s and Gender Centers: Benefits, Challenges, and Future Prospects
Bibliographic record
Abstract
Women’s and gender centers have provided a home for feminist activism, education, and empowerment on the college campus since the 1970s. Recently, some women’s and gender centers have undertaken practices of gender inclusion—expanding their missions and programming to include cisgender men and trans* people of all genders. This exploratory study sought to document these practices and to give voice to the challenges and benefits that centers derive from including those who do not identify as women in their work. Twenty professional staff at campus-based women’s and gender centers were interviewed for this study. Participants described how they are enacting gender inclusivity and named the benefits of bringing people of all genders into the work of advancing gender equity on campus, such as increased numbers of students actively participating in the center’s work and broadening the dialogue on women’s issues. Challenges included an ongoing need to protect women’s space for empowerment and the stress of an increased workload due to expanded programming. Overall, participants were positively inclined toward gender inclusion and felt it represented new and exciting possibilities for coalitional awareness and change on campus.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".