Water People: Genocide, Children, and Nature in Canadian Residential Schools
Bibliographic record
Abstract
This paper focuses on connections between Indigenous Peoples and water to better understand the cultural destruction wrought by residential schools. We begin with an overview of how genocide studies address connections between childhood socialization and the natural world, and how human links to nature are impacted by genocide. This point is further illustrated through a discussion of the centrality of water within Anishinaabe culture, whereby relationships with water contribute to the process of becoming Anishinaabe. We note that childhood is an important time for the development of water relations. Residential schools interrupted these relationships by transforming water into a tool of violence, compelling Indigenous children to reimagine their connection to water through the lens of settler capitalism and Judeo-Christian morality. Drawing on Survivor testimonies of their childhood and relationships with water before, during, and after residential schools, we demonstrate how their ability to co-create themselves as Anishinaabe with water was stifled in residential schools. We also note Survivor testimonies where water is engaged to resist assimilation and heal trauma, evidencing how the natural world can play an ambivalent role in projects of cultural destruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".