Book Review of Gillmor, Don. (2018). To the River: Losing my Brother. Toronto: Random House Canada.
Bibliographic record
Abstract
To the River: Losing my Brother," is a poignant blend of memoir and exhaustive study of suicide.When his younger, middle-aged brother, David, is reported missing in Whitehorse, Yukon, the author travels north to find him.Shortly after his arrival, David's body is pulled from the Yukon River and his death is tragically ruled a suicide.Overcome with grief and confusion, Gillmor embarks on a painful journey to identify what led his brother to take his own life.Along the way, he discovers David's tragic death is not an anomalyunprecedented and unrivalled numbers of white, heterosexual middle-aged men are dying by suicide.Driven by the loss of his brother, Gillmor sets out to deconstruct these disturbing trends and determine whether David's death could have been prevented.The first half of Gillmor's book weaves a contrasting narrative between uplifting childhood memories and gut-wrenching discoveries about his brother's dysfunctional life in Whitehorse.The second half of the book provides a thorough, data-based analysis of suicide among boomers, from which Gillmor connects not only to David's death, but also to the suicides of his friends and peers.The result is an aching depiction of grief, loss and "middle-aged male despair" bolstered by the few truths we have about suicide-and hindered by the many unknowns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.027 |
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".