Book Review of Robertson, David A. (2020). Black Water: Family, Legacy and Blood Memory. Toronto: HarperCollins Publishers.
Bibliographic record
Abstract
is a poignant memoir that delves into themes of intergenerational trauma, cultural identity, and the enduring impact of colonialism on Indigenous communities.Robertson's narrative takes readers on a journey of self-discovery as he reconnects with his Cree heritage and reconciles with his family's history, touching upon the legacy of Residential Schools and the loss of their ancestral trapline due to historical policies.Through his skillful storytelling, Robertson successfully conveys the significance of blood memory and the profound connection between Indigenous people, their history, and the land.The memoir offers a powerful exploration of Indigenous identity and highlights the ongoing consequences of the Residential School system on families, communities, and the ties between land, language, and culture.In his memoir, Robertson intricately weaves together his journey, recollections of a fatherson trip to a northern Manitoba trapline, and reflections on his childhood experiences.He shares that his upbringing lacked any awareness or comprehension of his family's Indigenous heritage, leading to a childhood marked by anxiety and a sense of uncertainty.Raised without his Indigenous heritage, Robertson's writing immerses readers in a journey as if they are sitting alongside him and his father on the way to the trapline.His narrative skillfully bridges the gap between Indigenous
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".