The Canadian Northern Corridor Community Engagement Program: Results and Lessons Learned
Bibliographic record
Abstract
The Canadian Northern Corridor (CNC) Research Program is an investigation of the feasibility, desirability, and acceptability of infrastructure corridors in advancing integrated, long-term infrastructure planning and development in Canada. The Corridor Concept involves a series of multi-modal rights-of-way across mid- and northern Canada — connecting all three coasts and linked to existing corridors in southern Canada — for the efficient, timely and integrated development of trade, transportation, and communications infrastructure. Corridors are expected to make public and private infrastructure investments more attractive by reducing the uncertainty associated with project approval processes; sharing the costs associated with establishing and administering rights-of-way; decreasing negative environmental impacts; and moving to a more strategic, integrated and long-term approach to national infrastructure planning and development. A key outcome of corridor development is decreasing the existing infrastructure gap that persists between northern and southern Canadian regions and communities. The causes of this gap are complex and will require a diverse set of tools and solutions to resolve; the CNC is a useful conceptual tool to initiate discussions on northern infrastructure and to identify feasible and lasting solutions to address Canada’s infrastructure gap.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".