Book Review of Robertson, David A. (2020). Black Water: Family, Legacy, and Blood Memory. Toronto: HarperCollins Publishers.
Bibliographic record
Abstract
David Robertson's book, "Black Water" is an emotionally-healing memoir that intertwines his own lived experiences along with his father, and ancestors to regain his Indigeneity and live a life worth living in their eyes, especially within his role as a father.The story focuses on a trip to his father's trapline (per his request) in which Robertson is able to tether together the missing pieces of his upbringing, and repair the bond that fell victim to lost time.Throughout this journey "home" central themes of family, legacy, and "blood memory" reveal themselves.Moreover, Robertson illustrates the impact that assimilation had on Indigenous people and the intergenerational trauma that remains despite attempts to erase it from Canada's odious history.Therefore, he addresses the creation of harmful racial stereotypes remaining from the colonization of the Indigenous people, and how it influenced his own racial prejudices.Robertson uses a conversational style of writing with a relaxed tone that provides intimacy between the author and the reader similar to a personal conversation between friends.As a child, Robertson was kept in the dark regarding his Indigenous roots.His parent's decision to keep his identity hidden, was so as to not limit the various opportunities open to him, that would be revoked if he labeled himself an "authentically Cree" man.They acknowledged that the time in which he and his siblings grew up, as well as the neighbourhood they resided would not be so kind as to
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.036 |
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".