Bibliographic record
Abstract
This commentary draws on personal experiences, my time spent discussing acts of harm in the academy with activists, and a review of various incidences on issues of academic harm and responsibility. Over the last few years, I have observed numerous high-profile cases in anthropology – in various countries and various contexts – that have elicited a significant public response. Some frame this kind of harm as the proverbial ‘few bad apples’, an approach I reject as it ignores what enables harm. Alternatively, some attempt to use the idea of ‘academic freedom’ as a way to sidestep questions of interpersonal obligations. Recently, I have encountered this line of argument in defences made by some against allegations about John Comaroff, such as media pieces that I note have been later cross-posted to his own website (Comaroff 2022; Walsh 2022). Instead of settling into a debate about what is or is not ‘academic freedom’, I here highlight a different reorientation, a shift in framing: what I have called, in conversations with friends and collaborators, ‘academic responsibility’. This reminds us that whereas academic freedom is frequently framed as a freedom to or a freedom from, academic responsibility emphasises our responsibilities as scholars and the obligations which follow to others. This includes a refusal of what Zoe Todd (2019) calls a ‘failure of imagination’ – we can and must envision different ways of building scholarly spaces beyond what we ourselves have seen or experienced in the academy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.103 |
| Scholarly communication | 0.033 | 0.064 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.049 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".