The Political Economy of the Provincial Periphery: Analyzing the Avaricious Verities Underlying Resources for Freedom and the Uranium Industry in Northern Ontario
Bibliographic record
Abstract
Though the burgeoning of bipolarity in the post-World War II period brought with it lauded stability in the international system,1 for the region of Northern Ontario, the emerging Cold War and the ensuing urgency by the United States (US) for strategic staples2 proved portentous in its development. In 1952, US President Harry Truman disseminated the five volume findings of his presidential commission on natural resources deemed fundamental to ensuring US hegemony and sustaining its security in the Cold War.3 Referred to as Resources for Freedom, or simply the “Paley Report ” after the chair of the commission William Paley, it identified twenty-two “key ” natural resources the US required from foreign nations, thirteen of which were found in Canada: aluminium, asbestos, cobalt, copper, iron, lead, natural gas, newsprint, nickel, petroleum, sulphur, titanium, and zinc.4 The Paley Report, furthermore, identified a relatively novel commodity, uranium, as a prospective strategic staple to be procured from Canada.5 In the Cold War world of the 1950s, the exploration, exploitation, and exportation of nuclear commodities like uranium were of grave significance to the US. Alarmed that the former Soviet Union (USSR) had detonated its own atomic bomb in 1949 and was further
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".