Introduction: Sexual Harassment and Gender-Based Violence in Political Science Fieldwork
Bibliographic record
Abstract
Political scientists recently have taken great strides in addressing sexual harassment and assault in the discipline. Little has been said, however, about sexual violence that political scientists may confront during field research. Field research involves any data-collection activity that occurs away from a researcher’s home institution, including visiting a prominent archive, interviewing political elites, and conducting direct observation of political phenomenon, and fieldwork is widely considered essential to data collection and career development across political science subfields. Field researchers may experience numerous power disparities that put them at acute risk for sexual or gender-based violence in the field, and evidence suggests that such experiences are pervasive and professionally devastating. In an effort to reduce gender-based violence and discrimination across academic worksites, several disciplines and institutions have developed specific guidelines and protocols to prevent and address sexual harassment and assault during fieldwork (Berkeley PATH to Care Center 2020; University of California, Riverside 2018; University of Toronto, Department of Anthropology 2019; Woodgate et al. 2018). Political scientists, however, have largely failed to conceptualize field placements as work settings or to address gender-based violence during fieldwork in our curriculum, training, and policies. Instead, they rely on deeply held methodological fallacies that insist on a field researcher’s absolute privilege, trivialize experiences of sexual violence, and weaponize rape myths to portray survivors as professionally incompetent (Hunt 2022).
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".