Bibliographic record
Abstract
Having conducted research and taught courses in political anthropology for several decades, particularly on nationalism, populism, and ethnonational conflict, I have initiated, facilitated and been caught up in many difficult conversations on issues including identity (politics), rights, conflict and justice with my students and colleagues. My research on the dissolution of the former Yugoslavia and the Kitchener-Waterloo Mennonite Victim-Offender Reconciliation Program has taught me valuable lessons on how practices of repair have been embedded or emerged in response to tensions, conflict and trauma-induced experiences, histories and lived realities. These lessons have become especially relevant in the context of recent challenges to academic freedom that have emerged in response to the ongoing war in Palestine/Israel. Suspensions, censure and surveillance are some of the troubling consequences, which combined have had an enormous impact on pedagogy and scholarship, affecting faculty and students alike. In this context, I argue that practices reflecting the centrality of (and urgent need for attention to) academic freedom are enhanced through pedagogical instruction and illustrate several strategies I use in classroom settings. I also discuss how despite the fractious environment created, and in some cases inflamed by the ongoing conflict, faculty and student groups are endeavouring to create and test out safe and respectful spaces for critical thinking, vigorous debate, as well as sharing and listening. These and other locally inspired initiatives are important to building networks based on thoughtful discussion and analysis, allyship, activism and more. I conclude by reflecting on anthropological research on the ethics of repair and its potential to inform pedagogical strategies aimed at navigating increasingly fraught and vulnerable academic spaces.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.061 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.009 | 0.017 |
| 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".