The Capaciousness of No: Affective Refusals as Literacy Practices
Bibliographic record
Abstract
Abstract The authors considered the capacious feeling that emerges from saying no to literacy practices, and the affective potential of saying no as a literacy practice. The authors highlight the affective possibilities of saying no to normative understandings of literacy, thinking with a series of vignettes in which children, young people, and teachers refused literacy practices in different ways. The authors use the term capacious to signal possibilities that are as yet unthought: a sense of broadening and opening out through enacting no. The authors examined how attention to affect ruptures humanist logics that inform normative approaches to literacy. Through attention to nonconscious, noncognitive, and transindividual bodily forces and capacities, affect deprivileges the human as the sole agent in an interaction, thus disrupting measurements of who counts as a literate subject and what counts as a literacy event. No is an affective moment. It can signal a pushback, an absence, or a silence. As a theoretical and methodological way of thinking/feeling with literacy, affect proposes problems rather than solutions, countering solution‐focused research in which the resistance is to be overcome, co‐opted, or solved. Affect operates as a crack or a chink, a tiny ripple, a barely perceivable gesture, that can persist and, in doing so, hold open the possibility for alternative futures.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".