Our Shared Future: Windows into Canada’s Reconciliation Journey — A Review
Bibliographic record
Abstract
The challenges and complexity of the reconciliation process are still not well understood by a large number of non-Indigenous people in Canada. As a nation, we are attempting to grasp the intricacy of how to unravel and atone for the damage that has been done in establishing and managing the more than 130 residential schools in Canada. This not only impacted more than 150,000 First Nations, Métis, and Inuit children but destroyed generations of families that are still and will continue to be impacted for years to come. The official apology from Prime Minister Stephen Harper on June 11, 2008, to all Indigenous people in Canada for the atrocities of the Indian Residential Schools was the start of a very long and painful continuous journey. The 94 calls to action released in 2015 by the Truth and Reconciliation Commission provide a road map to a complex recovery process for Indigenous people across the country. In January 2018, Health Canada held a national panel discussion with Indigenous leaders and experts on the question “Reconciliation—What Does it Mean?” One of the main themes of reconciliation revolves around education, and, in order to stay focused, we must continue to educate Canadians, including police leaders and new recruits, as we move through the meandering path of econciliation. The book Our Shared Future provides an outstanding in-depth look through the windows into a number of individual perspectives on the reconciliation journey.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".