The impact of Title IX iterations on campus sexual misconduct reports per synthetic control in the United States
Bibliographic record
Abstract
Title IX regulation changes' impact on sexual misconduct (SM) reporting to the institutions of higher education (IHE) in the United States (US) remain poorly understood. To examine trends in the rates of SM reports submitted to each American institution's Title IX Office, we applied a synthetic control. US IHE members of the American Association of Universities comprised the 'treated' group, and Canadian IHE members of the Major Regional Associations were used to create a counterfactual proxy. Marginally significant increases (P = 0.08) in reports followed the 2017 Title IX guidance change (+ 1.18, + 4.51 and + 2.24 reports per 1000 enrolled students in 2017-2018, 2018-2019 and 2019-2020, respectively), and a marginally significant decrease (- 5.23 reports per 1000 enrolled students in 2020-2021) in SM reports to Title IX offices followed the 2020 Title IX iterations. Reporting and response structures, like those specified in Title IX iterations, may influence rates of SM reporting.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".