Book Review of Robertson, David A. (2020). Black Water: Family, Legacy and Blood Memory. Toronto: HarperCollins Publishers.
Bibliographic record
Abstract
Residential schools caused many Indigenous families to be ripped apart, languages to be lost, and changed how many Indigenous people view themselves.In David Robertson's memoir book "Black Water," we learn how intergenerational trauma has affected his family and how their family was able to heal.There were many themes within the book, including the subtitle: family, legacy, and blood memory.However, this review will focus on education, language, and stereotypes.Davidson shared many memories of himself growing up that could not have been easy to write, including the shame of denying that he was Indigenous.He showcased many examples of how colonization and residential schools have demonized Indigenous peoples.Through the book, we also see hope, love, and that some ancestry will not be taken away because of blood memory.Education and the importance of it was stressed throughout the book, starting with mentioning how important education was to Robertson's Nana (Sarah Robertson): Above all else, Nana wanted her children and grandchildren to get educated.She understood the necessity of education and the difficulties of being First Nations in the new world they faced.To the Olson girls, my cousins, she once said, "It's really nice that you want to learn about your Indian side, but don't let it get in the way of your education" (p.73).This quote showcases not only how Nana felt about Western education but also displays some
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.046 |
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".