Book Review of Cook, Sharon A. and Carson, Margaret. (2022). The Castleton Massacre: Survivors’ Stories of the Killins Femicide. Toronto: Dundurn Press.
Bibliographic record
Abstract
Stories of the Killins Femicide" tells the tragic story of a familicide that took place on May 2nd, 1963.In rural Castleton, Ontario, Robert Killins murdered his estranged wife, Florence, her daughter, Pearl, Florence's sister, Gladys, and Florence's young daughter, Patsy.Included in the murder was the unborn child of Pearl.The two survivors of this massacre, young Margaret and Brian, narrowly escaped the event and were taken in by their aunt and uncle in Calgary, Alberta.This book explores the traumas acquired by Margaret and Brian, highlighting the effects of domestic violence and the societal norms that failed to protect Florence and her family.This book places a large emphasis on trauma and resilience.Margaret and Brian, who were children at the time that their family was murdered, faced an enormous amount of trauma.Brian reported experiencing immediate reactions, including nightmares and insomnia.He explained, "Initially, I was given a bed in the basement.There were strange noises in the house… I would often jolt awake and lay in my bed in a cold sweat, my heart pounding, fearing for my safety from Harold [Brian's uncle], only because he resembled Robert" (pp.213-214).This fear and anxiety that the children experienced revealed their ongoing trauma.Brian recalls a time when he hid in
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.045 |
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".