Book Review of Robertson, David A. (2020). Black Water: Family, Legacy and Blood Memory. Toronto: HarperCollins Publishers.
Bibliographic record
Abstract
DavidRobertson's book "Black Water" is a memoir about intergenerational trauma and healing.He recounts his journey of understanding his family, identity, and reconnections with the land on which his Cree father, Don, had abandoned.The book's subtitle reveals its themes: family, legacy and blood memory.Robertson then reflects on the way his experiences of his family and social surroundings have shaped him and questions himself about what it means to be Indigenous.Moreover, he reveals the normalization of Indigenous peoples being portrayed as racialized and deprived.Through it, he divulges historical trauma and exposes the colonial violence against Indigenous people.Robertson has a unique style in his memoir; he recollects a father-son journey to the northern Manitoba trapline and entwines it with recollections of his memories and reflections from his childhood.He reveals to the readers that he was raised without knowledge or understanding of his family's Indigenous roots, fostering a childhood filled with anxiety and uncertainty (pp.12-13).More specifically, the book indulges itself as if the reader is sitting with Robertson and his father on the way to the trapline.
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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 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.022 |
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".