A pedagogy of mourning: tarrying with/in tragedy, terror, and tension
Bibliographic record
Abstract
In this text I offer a narrative reflection, as a teacher-traveler, on my live(d) experiences in a sometimes (always already) violent world. Preoccupied with the possibilities of the work of mourning, in the first two movements I draw on stories of my time teaching in China at the dawn of this millennium to tell tales of tragedy, terror, and tension that provoked strange pedagogical moments. I reflect on the difficulties and passions of live(d) pedagogies that crack open curri/culum to tarry with/in these provocative, generative spaces as places for mourning and connection, ambivalence and ambiguity. During the 3rd movement I join Butler’s questioning of ‘what counts as a grievable life’ as I attempt a textual encounter with an (un)grievable ‘other’. I ask: How might recognition of lives/deaths through the act of inscription and collective mourning be related to understanding human connection? In a pedagogical refrain, I draw Derrida into my conversation with feminist theory to ask seriously about the pedagogical potential of tarrying with/in mourning, particularly for peace education that prioritizes an awareness of human connection. What are the transformative possibilities of returning again to mourning? I conclude with a call from peace educators and other emerging epistemologies that challenge us to think differently about human connection in our pedagogical work. Above all this text is a provocation in/to difficult spaces of mourning and pedagogical movement.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".