Bibliographic record
Abstract
In this article I provide a review of two connected events. The first is the conference "Prairie Perspectives on Indian Residential Schools, Truth and Reconciliation," which was held in June 2010 in Winnipeg, Manitoba. This conference was just one of many concurrent events taking place at the Truth and Reconciliation Commission of Canada's first national event. Specific themes and aspects of the conference are covered here. Secondly, I parallel my discussion of the conference to my experiences with the national event - experiences can be complex and do not happen in isolation from the broader context around them. Overall, I argue that while the conference and the national event made some meaningful contributions to ongoing dialogue about reconciliation in Canada, it is clear that understanding how to deal with and discuss the conflict that arises from discussions of residential school, "race relations," and reconciliation more broadly is an ongoing learning experience. I offer some recommendations concerning how conflict could be better dealt with at future conferences and national events. Reconciliation processes can be more effective if there is not only space for dissent but, most importantly, that mechanisms are in place for encouraging productive discussions about the conflict that arises and that will continue to arise.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".