Informing Expansion of Gender Inclusive Data Collection in Post-secondary Education in British Columbia
Bibliographic record
Abstract
Aim/Purpose: To inclusively consider the diversity within student gender-identification at post-secondary institutions, we investigate expanding gender self-identification options on admissions forms; often the first point of student contact with campuses. Background: Even if inspired and motivated by inclusion, many of the gender categories in use presently have challenges, including conflating gender identity with sex assigned at birth, providing too many response options giving rise to ethical issues, and using outdated or misunderstood terms. Methodology: We conducted a sequential mixed-methods exploratory research design that consisted of interviews (n=9) with administrators in post-secondary institutions, followed by a survey of said administrators (n=21), and finally a survey of students (n=45). Contribution: The data detail experiences and inform best practices for ensuring gender inclusivity, specifically concerning students who identify as transgender or non-binary, when filling out forms. Findings: Results indicate that moving beyond binary gender categories entails a balance between (1) institutional issues of data integrity for effective use of gender data, and (2) providing flexible and inclusive options for gender-identification that extend within and beyond the gender binary to ensure students are counted where historically they have been invisible. Recommendations for Practitioners: To balance inclusivity and data management institutions may consider a two-part question, first asking about gender (woman, man, non-binary), and then asking about gender-identification experiences (yes/no). Recommendation for Researchers: As a system, we must find a way to balance inclusion with data management, and transgender and non-binary students must be free of administrative burdens in order to exercise their voice and access post-secondary education. Impact on Society: Collecting expanded gender categories in the school system is only the beginning of a shift in how transgender and non-binary students feel welcomed and supported on campus. The shift is critical to the focus and wellbeing of these students. Future Research: Future researchers, we suggest, may wish to focus on gathering examples of implementation of expanded categories and illustrations of how these data are used to inform and shape changes to policy, practices, spaces, services, and programs. More in-depth exploration of the inclusion of Two Spirit identities in ways that allow their identity to remain intact rather than partially represented in response to the gender question.
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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 | 0.055 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".