Feeling in Crisis: Vicissitudes of Response in Experiments with Global Justice Education
Bibliographic record
Abstract
This paper considers the nexus of affective engagement, a critical analysis of systemic discrimination, and reflexive self-implication in education committed to social change. I ask how our inherited models and theories of learning and teaching for social change can curb our vigilance vis-à-vis the ways libidinal dynamics organize our curriculum as affective wish and our pedagogy as affective defense. I focus specifically on the qualities of global justice education—the ethical and political stakes of this project, the theories of learning, pedagogical strategies and discourses of moral development it inherits—that render learning, teaching, and learning to teach (none of which are discrete) affectively and ethically fraught. The paper examines student writing samples from a mandatory pre-service course on social and global justice education, one designed to engage future teachers in considering the inequitable global distribution of precarity and recognizability (Butler) within contemporary contexts of neoliberal globalization and war. In the writing samples, students reflect on the challenges they faced making sense of representations of the ravages of militarized capitalist globalization, in particular the challenges of creating and facilitating curriculum within which their peers might encounter such difficult knowledge. I ask, firstly, what students’ narrations of teaching social justice tell us about what is difficult in studying/teaching the devastation of the contemporary militarized, imperial global capitalist order in which we are profoundly implicated as citizens in the global North. Secondly, what can student narrations of teaching social justice tell me about what is difficult in global justice teacher education, specifically the wishes, the anxieties and discursive foreclosures underpinning my own conduct of it in this course?
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".