Bibliographic record
Abstract
2This article takes up three questions. The first asks, in wonderment, how it could possibly happen that anyone––anyone at all––might willfully choose to end her or his own life? The second concerns why it is that, when they do occur, suicides and suicidal behaviors occur so disproportionately and so inexplicably among the young? Finally, we mean to take up a more localized and culture-bearing version of these same questions by asking how and why it could have come to pass that Canada’s young “First Nations ” persons take their lives in such outlandish numbers––suicide rates said to be the highest of any culturally identifiable group in the world (Kirmayer, 1994)? The broad thesis we intend to unfold in the pages that follow is that all of our best hopes of answering these nagging questions turn on first reversing their usual polarities, all in an effort to get clear about death’s opposite number––about our more mundane reasons for struggling, as we ordinarily do, to cheat death by surviving at almost any costs. Why, it is important to ask, do people have such an abiding commitment to their own persistence––a stake in their future that, as our own research has shown, appears to have regularly gone missing among those that actually do undertake to kill themselves (Chandler, Lalonde, Sokol, & Hallett, 2003)? We do, of course,
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.782 | 0.525 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".