Toward a More Gender-Inclusive Sexual Experiences Survey: Development and Preliminary Validation With Transgender and Gender-Expansive Survivors of Campus Sexual Assault
Bibliographic record
Abstract
We adapted the Sexual Experiences Survey (SES) to be more inclusive of transgender, nonbinary, and gender-expansive people and undertook preliminary validation of the measure. We added gender-neutral language, assault types thought to be more common and more emotionally impactful among these individuals, and coercion tactics specific to transgender, nonbinary, and gender-expensive people. We piloted this Gender-Inclusive SES (GI-SES) with an online survey of transgender and gender-expansive undergraduates who experienced campus sexual assault. We assessed acceptability via rapid qualitative analysis of feedback. We gathered preliminary evidence of validity by calculating the agreement in assault type and coercion tactic between open-ended descriptions of the assault and the GI-SES response, as well as by testing four hypotheses about the relative frequency and severity of different types of assaults within the sample. Three hundred eighty-eight transgender, nonbinary, and gender-expensive students completed the survey. Qualitative responses indicated that participants felt the GI-SES was inclusive and respectful. Results were partially consistent with hypotheses supporting validity, and there was 90.9% agreement in assault type and 85.9% agreement in coercion tactics between GI-SES responses and written narratives. The GI-SES provides researchers, clinicians, and service providers with a tool, preliminarily validated with a trans and gender-diverse college sample, to capture the unique experiences of transgender, nonbinary, and gender-expensive sexual assault survivors.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".