On not wanting it to count: reading together as resistance<sup>1</sup>
Bibliographic record
Abstract
Reading groups can be spaces of resistance, both from the competitive performances of some classroom seminars and from the calculative fields of neoliberalizing departments and universities. As graduate students, we offer this intervention as a consideration of the bodily politics of academic reproductions. In discussing the embodiment of textual practices in seminar and in reading groups, we point to monologue, ‘trashing’ criticism, and obscurity as practices habituated in the classroom seminar. We discuss how reading groups contest ‘proper’ knowledges, while enabling a multiplicity of textual, bodily practices. Finally, we consider how certain reading practices potentially de‐stabilise neo‐liberal subject formation in the academy. We discuss why we do not want reading groups to count, as a strategy for resisting accounting and accountable regimes in our departments and universities.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".