Building Other Worlds in Education Through the Radical Potency of Despair
Bibliographic record
Abstract
Over the past few decades an overwhelming sense of despair has infiltrated education. This despair, manufactured by education’s thoughtless submission to neoliberal logic exacerbates the already compromised conditions in education. Drawing on parts of my dissertation, “Cruel Optimism,” Burnt-out-souls, and the Ruptured Fantasy of Education (2021), my keynote address responds to the conference provocations, which ask us to think about ethical possibilities in education, and educators’ desires to build other worlds amidst the present conditions of education in the ruins. Inspired by F. Tony Carusi’s (2017) suggestion that despair in education might “be a condition in which ethical teaching finds new movement,” I register the radical potency of despair as a force which works to reckon with the “cruel optimism” of education (p. 642). Consequently, my talk does not join the discourse of hope in education, rather, I suggest that it is not hope that ignites educators who keep fighting the ruins of education but rather our despair. Our despair moves us. Our despair motors us onward, despite our burnt-out-souls, as we continue to stand up for the potential in education.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.109 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".