Northern Corridor Research Program: Phase 2 Final Report
Bibliographic record
Abstract
The Canadian Northern Corridor is an idea that responds to Canada’s need to increase interregional and international trade, provide services to northern communities, and establish a broadly accepted approach to large-scale infrastructure development. Since 2015, the School of Public Policy at the University of Calgary has undertaken research and public engagement sessions to study the feasibility, acceptability, and desirability of a coherent and unified approach to national and regional infrastructure development in Canada. This paper stands as a final abbreviated summary report of the research and engagement program to date. The entire program comprises well over 40 individual studies conducted by over 50 contributing researchers and authors across eight research themes over the past 8 years. As such, this summary final report provides only a very basic overview of the research program. At the most fundamental level, the research conducted under this program suggests that a large-scale corridor concept is challenging to conceive, in both theory and practice for mid- and northern Canada. For that reason, we recommend a segmented corridor approach focused on development initiatives which are already gaining public acceptance and that communities identify as key priorities, such as digital infrastructure. One early priority could be the digitization of highways and roadways to enhance safety while travelling and to digitally connect communities. As such, a corridor approach must reflect a holistic strategy addressing the existing shortcomings related to the infrastructure gap in mid- and northern Canada which contributes to problems around unreliable transportation pathways, digital connectivity, food insecurity, inadequate housing, and lack of healthcare and education services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.051 |
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".