Revisiting “Pedagogy of Discomfort” Through the Combined Lenses of “Inconvenience” and “Affective Infrastructure”: Pedagogical and Political Insights
Bibliographic record
Abstract
This paper seeks to revisit the concept of “pedagogy of discomfort” through the combined lenses of Lauren Berlant’s work on “inconvenience” and recent theorization of “affective infrastructure” to clarify how an infrastructural understanding of “discomfort-as-inconvenience” might provide deeper insights about the pedagogical and political risks and possibilities of discomfort. In particular, the paper highlights three insights: first, it expands our understanding of discomfort by situating it in the broader context of the inconvenience of other people, as an ethics and politics of coexistence; second, it calls for a contextual approach of a pedagogy of discomfort that examines discomfort as a multifaceted affective event entangled with other material, social, and political elements; and third, it enables educators to create environments that could enrich the moral and political potential of a pedagogy of discomfort, by paying attention to the affective conditions in which students and educators find themselves when they encounter different manifestations of discomfort-as-inconvenience.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".