Implications of a Northern Corridor on Soft Infrastructure in the North and Near North
Bibliographic record
Abstract
Disparities in health care, education and employment, housing and social welfare have long been documented in Northern Canada. These disparities have been linked to colonialism, ineffective social policy, uneven development and the high costs of service delivery and infrastructure in northern regions. This literature review aims to present a comprehensive understanding of existing research on the current state of soft infrastructure and its deficits in Canada’s North and near-North regions. This scoping review contributes to a larger project led by the University of Calgary’s School of Public Policy and their Northern Corridor Research Program, a project which aims to evaluate the establishment of permissible corridors in Canada. These corridors provide defined multi-modal rights-of-way with accompanying regulatory and governance structures. Specifically, the term “soft infrastructure,” for the purposes of this review, refers to health care, housing, education, employment, jobs training and emergency services. The implications of these deficits in terms of economic and social opportunities in northern regions are discussed in relation to current research. Additionally, the ways in which these deficits relate to current hard infrastructure assets and deficits are assessed based on the reviewed literature. Finally, the costs, benefits and opportunities associated with the proposed Canadian corridor with regards to soft infrastructure deficits and needs are addressed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".