Complicating Theory through Practice: Affirming the Right to Die for Suicidal People
Bibliographic record
Abstract
Currently, suicidality is inadequately engaged with in suicide prevention methods. The key focus is on preventing people from dying, rather than validating suicidal ideation as a legitimate experience. As Alexandre Baril (2017; 2018; 2002) argues, in this refusal to validate suicidality, suicidal people are subjected to suicidism – a term Baril coined to describe the oppression suicidal people face which silences them and views their desire to die as illegitimate. Baril (2017) argues for a harm reduction approach to suicide which not only recognizes the validity of suicidality, but also supports suicidal people should they choose to die. In this paper, I seek to highlight Baril’s argument on affirming the choices of suicidal people through an engagement with three of his ideas: epistemic violence, biopower, and the injunction to happiness. While I agree with Baril’s harm reduction approach to suicide, I also contend that his argument becomes more complex when shifting from theory to practice. Therefore, alongside Baril’s work, I highlight the work of Kai Cheng Thom, who recognizes that failing to fight for suicidal people is equally as ableist as failing to listen to them.
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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 | 0.050 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.114 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.012 | 0.017 |
| 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".