The theory, practice, and potential of regional development the case of Canada
Bibliographic record
Abstract
Canadian regional development today involves multiple actors operating within nested scales from local to national and even international levels. Recent approaches to making sense of this complexity have drawn on concepts such as multi-level governance, relational assets, integration, innovation, and learning regions. These new regionalist concepts have become increasingly global in their formation and application, yet there has been little critical analysis of Canadian regional development policies and programs or the theories and concepts upon which many contemporary regional development strategies are implicitly based.This volume offers the results of five years of cutting-edge empirical and theoretical analysis of changes in Canadian regional development and the potential of new approaches for improving the well-being of Canadian communities and regions, with an emphasis on rural regions. It situates the Canadian approach within comparative experiences and debates, offering the opportunity for broader lessons to be learnt.This book will be of interest to policy-makers and practitioners across Canada, and in other jurisdictions where lessons from the Canadian experience may be applicable. At the same time, the volume contributes to and updates regional development theories and concepts that are taught in our universities and colleges, and upon which future research and analysis will build
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".