Book Review of Robertson, David A. (2020). Black Water: Family, Legacy and Blood Memory. Toronto: HarperCollins Publishers.
Bibliographic record
Abstract
David Robertson's "Black Water: Family, Legacy and Blood Memory" is a profound memoir touching on the many complex aspects of family and claiming one's heritage.Robertson dives into the effects of intergenerational trauma, as well as the pain of lacking one's true identity while perpetuating racist and negative views on Indigenous peoples and himself.His Cree father and white mother agreed to withhold their First Nations background from their children until they were older, though their long separation furthered the disconnection Roberston felt towards his identity.Reclaiming aspects of himself that had been kept hidden becomes a centerpoint for Robertson, as well as highlighting how the effects of colonialism continues to affect Indigenous peoples today.Reconciling with his father, Don, whom he spent ten years without, furthers his desire to reconnect with his roots, as well as enhances the revelation of his Cree heritage.Robertson imparts a unique wisdom to the familiar narrative of reclaiming one's heritage and finding the true meaning of family and home.His memoir expresses both the personal revelations with the universal experience of passing down family legacies to future generations.In the beginning of the memoir, Robertson paints a picture of his upbringing, a prominent theme throughout the entire memoir, including the painful and long separation of his parents at a young age as his mother raised him on her own.He expresses that being raised solely by his
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".