Relations of care and affirmative disruptions in academia: A conversation with Katie Strom
Bibliographic record
Abstract
This article features an intra-view with critical posthumanist Professor Kathryn (Katie) Strom and the co-editors of the intra-view section of this journal Jacky Barreiro and Magali Forte. Throughout their conversation, Strom shares her determination to co-create relations of care and affirmative disruptions in the neoliberal context of academia while giving concrete examples and explaining how her feminist praxis evolved. The three authors discuss several initiatives Strom and others have implemented, inspired by Braidotti’s (2019) notion and praxis of affirmative ethics among other feminist scholarship, to create spaces of support for graduate students and early career scholars. This piece encourages readers to view and understand feminist processes as affirmative disruptions to foster affective flows, challenge conventions, and inspire innovative scholarly pursuits.
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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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.051 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 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".