Emotional configurations of politicization in social justice movements
Bibliographic record
Abstract
Purpose This paper aims to trace how emotion shapes the sense that is made of politics and how politicization can remake and re-mark emotion, giving it new meaning in context. This paper brings together theories of politicization and emotional configurations in learning to interrogate the role emotion plays in the learning of social justice activists. Design/methodology/approach Drawing on sociocultural learning perspectives, the paper traces politicization processes across the youth climate movement (using video-based interaction analysis) and the animal rights movement (using ethnographic interviews and participant observation). Findings Emotional configurations significantly impacted activists’ politicization in terms of what was learned conceptually, the kinds of practices – including emotional practices – that were taken up collectively, the epistemologies that framed social justice work, and the identities that were made salient in collective action. In turn, politicization reshaped how social justice activists made sense of emotion in the course of activist practice. Social implications This study is valuable for theorizing social justice learning, so social movement facilitators and educators might design spaces where learning about gender, racialization, colonialism and/or human/more-than-human relations can thrive. By attending to emotional configurations, this study can help facilitate a design that supports and sustains learning for justice. Originality/value Emotion remains under-theorized and under-analyzed in the learning sciences, despite indications that emotion enables and constrains particular learning opportunities. This paper proposes new ways of understanding emotion and politicization as co-constitutive processes for learning scientists interested in politics and social justice.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".