Mapping affect in critical moments of schooling for disenfranchised students
Bibliographic record
Abstract
Students disenfranchised from school have something important to teach us about how the education system might be shaped more equitably. Currently, meta-narratives of student failures and pathologies prevail and are reinforced by dominant behaviourist teaching practices. In this action research project, we sought to understand how teachers might inquire with students about critical moments from their past schooling experiences. Working within Alternative Education settings, students and teachers inquired together using a variety of mediated arts-based methods. In our analysis, I-poems illuminated the power of affective spaces to shape student identities. We identified that affect and disenfranchisement were connected through lived schooling experiences of shame, exclusion and racism. In addition to critical moments, we identified there were microaggressions in the everyday of schooling experiences, and we explored the healing power of microaffirmations in student–teacher relationships through the practices of emergent listening and relational pedagogy. Students’ affective storytelling and the analysis from this research invites us to suspend our attachment to dominant behaviourist approaches and modern, industrial concepts of schooling and consider alternatives. We invite educators to consider alternative approaches to student–teacher relationships by leaning into lessons taught from students in this research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".