Regional Development and Policy in Norway
Bibliographic record
Abstract
This paper is prepared for a workshop on Peripheral regions to be arranged by INRSUrbanisation, Montreal in October 2001. The workshop is one part of a larger research programme by INRS that aims to investigate employment growth and development policy in peripheral regions in Canada, with a special focus on Quebec and \nNew Brunswick. The goal of the programme is among other things to identify approaches to regional economic development, which may be able to revitalise the peripheral regions in this part of Canada. \nOne part of the research programme includes international comparisons in which \nregional development trends and policies in Northern Europe (Scotland, Norway, \nFinland and Sweden) are measured up to those in Canada. The part will identify \nemerging economic development policies in peripheral areas in these countries and \nevaluate their relevance to the Quebec context. This paper gathers relevant information on regional development and regional policy in Norway to be employed in such \nan exercise. \nIn order to achieve comparability and address the need of the Canadian research \nprogramme, this paper follows as far as possible the ‘framework for expert reports’ \ndrawn up by the organisers of the workshop. The report first describes the general \nregional development and development policies in Norway, and then focuses specifically on development trends and policies in Northern Norway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".