Melting Pedagogies in Times of Despair: Engaging With and Through Rossiter’s Ethics
Bibliographic record
Abstract
Amy Rossiter’s body of work has had profound pedagogical and theoretical impacts on the lives and work of the authors of this paper. Using a relational methodology with letters as a starting point, we interview each other about Rossiter’s impact on each of us, and continue this engagement with letters throughout the article. With particular focus on despair in the classroom, we extend Rossiter’s contributions in conversation with Cvetkovich, Ahmed, and other queer and critical affect theorists to think through how to encounter that despair with the use of “melting pedagogies.” We illustrate some of the ways that we try to facilitate this melting, knowing that this work is always partial, contingent, and located in our own practices in the context of despair and hope. We finally consider what scholars and educators can learn and who they can become through ongoing and deliberate engagement with Rossiter’s deep and incisive contributions to ethics and a commitment to social justice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".