Regional governance change in Northern Norway. Insights for Northern Ontario, Canada
Bibliographic record
Abstract
Northern Ontario has been inadequately governed, perpetuating chronic health, social and economic issues. Recent policy discourse has suggested that the region take more control through the development of new regional governance or governments. The region should also look to other Northern jurisdictions for ideas. \n\nThis comparative case study examined the state of regional governance in two Northern regions, comparing the calls for regional governance change to more effectively administer Northwestern Ontario (as a part of Northern Ontario) against the Norwegian state-mandated amalgamation of Troms and Finnmark Counties (as part of Northern Norway). Six public officials– elected officials (politicians) or public servants (bureaucrats)– were interviewed in Northwestern Ontario and four were interviewed in the former Troms and Finnmark Counties. \n\nInformants in both countries validated the concept of Northern alienation and generally agreed that better regional governance and less central control was needed. Important considerations from Norway experience’s could inform Northern Ontario should it embark on regional governance change, including: consider a collaborative approach rather than a top-down, forced amalgamation; avoid determining the “form before function”; consider a “place-based” approach; consider regional rivalries and the impact of “re-centralization” to new capitals; include an external perspective; and involve Indigenous people from the beginning.\t\nFinally, in both Northern Norway and Northern Ontario, the most important overarching observation may be that public and Indigenous governance remains on separate tracks. This is of greater concern to Northern Ontario, where public regional governance appears to be stagnant while Indigenous governance continues to evolve.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".