Subarctic Corridors in Northern Quebec: Is the Canadian Northern Corridor Concept Aligned with Quebec’s Historical Development?
Bibliographic record
Abstract
Proposals for infrastructure development in Canada’s North are gaining political traction, including a corridor connecting the northern regions of each of the country’s provinces. Quebec is Canada’s largest, northernmost province and would be pivotal in the construction of the corridor. Examining the historical phases of Quebec’s northern development is crucial in assessing the challenges ahead. This paper groups Quebec’s infrastructure developments into three main phases, synthesizes each phase, and critically compares them to the proposed Northern Corridor concept (NCC). No research has yet examined the NCC’s complementarity with Quebec’s history of northern infrastructure development. While previous phases could be categorized as intraprovincial penetration corridors linking northern to southern Quebec, the NCC aims to develop an interprovincial economic corridor for landlocked provinces to be able to gain better sea access. Obstacles arising from the conciliation of past developments with the NCC include the unfitness of using existing infrastructures in Quebec for a Pan-Canadian corridor and differing development trajectories at the provincial and federal levels. Three route options for the NCC in Quebec are presented in this study. More generally, this paper outlines difficulties specific to subarctic remote corridor development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".