Widening Scripts
Bibliographic record
Abstract
Widening Scripts: Cultivating Feminist Care in Academic Labor is addressed to scholars, educators, and students devoted to the struggle against precarity, atomization, and the commodification of knowledge. Through shared reading, discussion, and reflection, and gathered around a shared interest in feminist theory and politics, the authors discovered a model of care within academia that helped them to sustain their opposition to dominant academic practices that are diminishing, competitive, and exploitative. In this book, the authors narrate that discovery and the realization of a desire to share in the assembling of a collective feminist survival kit. In Living a Feminist Life, Sara Ahmed offers a wide-ranging killjoy survival kit that includes books, things, tools, time, life, permission notes, other killjoys, humor, feelings, and bodies. As a response to the stress, strain, and profound grief produced by the COVID-19 pandemic, with its viral acceleration of crises already endemic to neoliberal capitalism, the authors mined an evolving cluster of decolonial feminist texts in an attempt to find meaning, encounter moving premonitions, and engage with radical instigations to thought. By co-creating a survival kit through sustained collaboration during the pandemic, they develop a sense of the value of experimentation and risk-taking and learn how to cultivate an inclusive space that allows them to express their views, reclaim accountability, and learn confidently from each other. Widening Scripts combines collaborative feminist theory, acts of care, and critical dialogue in an effort to open up decelerated, altruistic, and connected ways of doing academic work together.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".