Sexual Harassment and Violence in the Practice of Anthropology: Creating Safe Conversational Spaces for CASCA Members
Bibliographic record
Abstract
The Canadian Anthropology Society is working to address sexualharassment and violence at institutional and community-based settings whereanthropologists undertake their work. In 2021, the newly formed SexualHarassment and Violence Working Group held a roundtable at the CASCAconference to start a conversation about sexual violence among CASCAmembers and to workshop best practices to prevent, disrupt, and respondto incidents of sexual harassment and violence that CASCA members mayexperience or observe. Here, the Working Group summarizes the process ofplanning, implementing, and following up the roundtable, focusing on specificactions taken by the organizers to ensure a safe conversational space before,during, and after the event. We demonstrate how the roundtable aligns withinthe larger framework of CASCA’s institutional history and future. The goalof this report is to provide a framework for convening difficult conversationsin professional settings, especially in an online environment. We providerecommendations to this end, and emphasize the need to hold furtherconversations to combat the air of silence that remains, even in a post-#MeTooworld, surrounding sexual violence in anthropology.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.069 | 0.059 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".