Critical Suicide Notes: On Witnessing and Prefigurative Politics
Bibliographic record
Abstract
This paper reimagines the study of suicide as a critical, relational practice rooted in solidarity and transformative possibilities. Moving beyond the limitations of conventional suicidology, this work emphasizes the importance of attending to the broader social, political, and structural contexts that shape experiences of suicidality. By framing this work as a collection of “notes,” this paper calls for an approach that notices, marks, and responds to both the violence and resistance inherent in these experiences. This paper introduces witnessing, dreaming, and prefiguration as key methodologies for Critical Suicide Studies. Witnessing is conceptualized as an active and relational practice that centers on the lived realities of those affected by suicide, making their stories and the systems of harm that often go unaddressed, visible. Dreaming involves imagining futures beyond survival, where care and justice guide collective responses. Prefiguration focuses on enacting these futures in the present, embedding relational and community-based approaches in the everyday practices of suicide care and research. Through these practices, this paper explores how Critical Suicide Studies can move from critique to action, creating conditions in which responses to suicide are life-affirming, relational, and grounded in mutual care. This work aspires to cultivate spaces for collective healing, dignity and transformative change.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| 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".