Northern Ontario in Historical Statistics, 1871–2021
Bibliographic record
Abstract
Based on original historical tables, Northern Ontario in Historical Statistics, 1871–2021 offers an overview of major long-term population, social composition, employment, and urban concentration trends over 150 years in the region now called “Northern Ontario” (or “Nord de l’Ontario”). David Leadbeater and his collaborators compare Northern Ontario relative to Southern Ontario, as well as detail changes at the district and local levels. They also examine the employment population rate, unemployment, economic dependency, and income distribution, particularly over recent decades of decline since the 1970s. Although deeply experienced by Indigenous peoples, the settler-colonial structure of Northern Ontario’s development plays little explicit analytical role in official government discussions and policy. Northern Ontario in Historical Statistics, 1871–2021, therefore, aims to provide context for the long-standing hinterland colonial question: How do ownership, control, and use of the land and its resources benefit the people who live there? Leadbeater and his collaborators pay special attention to foundational conditions in Northern Ontario’s hinterland-colonial development including Indigenous relative to settler populations, treaty and reserve areas, and provincially controlled “unorganized territories.” Colonial biases in Canadian censuses are discussed critically as a contribution towards decolonizing changes in official statistics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".