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: Family, Legacy and Blood Memory," is a powerful, heart-wrenching memoir that discusses his life journey and reconnection to his identity, Indigenous culture, and family.Robertson displays a profound level of vulnerability in sharing his story, the stories of his family members, and his honest thoughts, reactions, and responses to the challenges he faced.Robertson appears to be very transparent, such as when he describes his challenges with anxiety and his responses to confronting stereotypes (pp.24, 179).It is appreciative that Robertson describes many situations and his responses, but also contrasts his past beliefs and his current reflections in detail, allowing the reader to accurately envision, and at times even feel, the emotions and experiences being described.Robertson's discussion of the importance of truth and understanding identity speaks to his intent of sharing his story and may inspire others to explore their identity, family history, and cultural connections as well. Robertson begins his book by sharing various examples from his childhood and adulthood.He describes the shame he felt regarding his Indigeneity, his challenges and responses to racism and oppression, and how this impacted his self-image and understanding of his identity over time.These challenges are important to understand the motivation for Robertson to explore this through-
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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 | 0.007 |
| 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) | 0.040 | 0.021 |
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".