Making Sense of Critical Suicide Studies: Metaphors, Tensions, and Futurities
Bibliographic record
Abstract
Critical suicide studies is a relatively new area of research, practice, and activism, which we believe can offer creative new vantage points with which to ‘think’ suicide into the future. We present findings from a qualitative research study undertaken to understand how critical suicide studies is being conceptualized by those who draw from this orientation. Semi-structured interviews were conducted with nine scholars, practitioners, activists, and/or those with lived and living experience of suicidality. To analyze the data, we used reflexive thematic analysis and drew on a social constructionist orientation. We discovered that metaphors were an important way of conceptualizing and reflecting upon critical suicide studies. Four themes were generated: critical suicide studies is a site of respite and fortification; critical suicide studies is a felt experience; critical suicide studies is a desire line; critical suicide studies is yearning. We contend that the dominant language available to describe suicide and suicide prevention might not be adequate for expressing the complexities and contradictions of suicide prevention practice or suicide’s ultimate unknowability. We call for more diverse, inclusive, and expansive frameworks for understanding and responding to suicide and show the potential of joining other critical scholars and social movements to build a more just, caring, and inclusive world.
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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 | 0.086 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.032 | 0.169 |
| Scholarly communication | 0.029 | 0.037 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".