Bibliographic record
Abstract
Based on extensive fieldwork and oral history, The Terms of Our Surrender is a powerful critical appraisal of unceded indigenous land ownership in eastern Canada. Set against an ethnographic, historical and legal framework, the book traces the myriad ways the Canadian state has successfully evaded the 1763 Royal Proclamation that guaranteed First Nations people a right to their land and way of life. Focusing on the Innu of Quebec and Labrador, whose land has been taken for resource extraction and development, the book strips back the fiduciary duty to its origins, challenging the inroads which have been made on the nature and extent of indigenous land tenure—arguing for preservation of land ownership and positioning First Nations people as natural land defenders amidst a devastating climate crisis. It offers a voice to the Innu people, detailing the spirituality practices, culture and values that make it impossible for them to willingly cede their land. The text is intended to bridge the gap in knowledge between legal practitioners and those working at the intersections of human rights, social work and public policy. The book offers a potent template for how we can use the law to fight back against the indignities suffered by all indigenous peoples.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".