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: Family, Legacy and Blood Memory" is a unique, self-reflective memoir in which he utilizes storytelling to share how his father's experiences of cultural revitalization and colonialism impacted him.Robertson narrates his pursuit to reestablish his identity, restore his cultural ties, and address intergenerational trauma as he alternates between revisiting his childhood memories and returning to the trapline with his father.The interconnectedness of family legacy, personal experiences, and historical events is revealed in this introspective exploration.Prominent throughout the book are themes including Indigenous identity, family, colonization, blood memory, and the significance of oral tradition, all collectively contributing to the book's overarching essence.Furthermore, the book illustrates the enduring resilience of Indigenous communities and underscores the significance of reconciliation.This compelling book provides readers with an intimate description of the author's experiences, fostering an appreciation for the diverse perspectives of Indigenous individuals in Canada and prompting self-reflection regarding their roles in advancing decolonization.Robertson reflects on his family's past and how specific locations, experiences, and individuals have provided him with insights into the significance of his Cree heritage.Throughout
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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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.020 |
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".