Preface: Towards Equality of Opportunity
Bibliographic record
Abstract
iN 2012, we Published a series of opinion pieces in Canada's national newspapers expressing our outlook on the future of Aboriginal affairs in Canada.At the time, the papers were full of stories about the dreadful housing conditions and infrastructure crisis at the Attawapiskat First Nation in northern Ontario, complete with overheated rhetoric and finger pointing.On February 17, in the Globe and Mail, we outlined the two camps of public opinion in "We Are All Responsible for the Plight of Canada's First Nations": there were those who believed that responsibility lay with the Government of Canada and those who claimed it lay with First Nations themselves.The first camp, composed of a number of First Nations leaders and many non-Aboriginal Canadians, argued (then as now) that Ottawa should take primary responsibility for the mess.It had, after all, created the Indian Act in 1876 (the federal statute dealing with status, local government, and the management of reserve lands and communal monies), the reserve system, and residential schools.The federal government needed to move quickly to address the dependency and despair that characterized First Nations communities.It needed to provide equality of result to remove what many considered a black mark on the nation.The opposing camp argued that Aboriginal people needed to clean their own house.They needed to root out corrupt politicians, abandon
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".