Valley of the Birdtail : An Indian Reserve, a White Town, and the Road to Reconciliation collection
Bibliographic record
Abstract
Andrew Stobo Sniderman is a writer, lawyer and Rhodes Scholar. His profile of Canada’s Truth and Reconciliation Commission on Residential Schools won the award for best print feature from the Canadian Association of Journalists. Mr. Sniderman has argued before the Supreme Court of Canada, served as the human rights policy advisor to the Canadian Minister of Foreign Affairs, and worked for a judge of South Africa’s Constitutional Court. Prof. Sanderson is Beaver Clan, from the Opaskwayak Cree Nation. He is deeply engaged in Aboriginal issues from a policy perspective. He was a Senior Advisor to the Government of Ontario. His research areas include Aboriginal and Indigenous legal theory, as well as private law and public and private legal theory. Mr. Sniderman and Prof. Sanderson wrote the award winning book Valley of the Birdtail: An Indian Reserve, a White Town, and the Road to Reconciliation (2022), winner of two awards in 2023: The Stubbendieck Great Plains Distinguished Book Prize from the University of Nebraska-Lincoln Center for Great Plains Studies, and the J.W. Dafoe Book Prize; it was also a finalist for the 2023 Shaughnessy Cohen Prize for Political Writing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".