Why critical literacy should turn to ‘the affective turn’: making a case for critical affective literacy
Bibliographic record
Abstract
The central argument of this essay is that critical literacy with a rationalistic bent may not enable us to cope with ethical dilemmas in our responsiveness to human sufferings. I argue that critical literacy education would benefit from turning to the recent scholarship on affect/emotion studies. I draw upon the works of Sara Ahmed – one of the leading contributors to critical affect studies – to shed light on what is called a regulatory power that creates a schism in our responsiveness to violence and suffering. The key contribution of this essay is to present an actionable framework of what I describe as critical affective literacy. To delineate this framework, I present four pedagogical principles, along with examples of instructional activities.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.103 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".