Peripheral but Vigorous, Southwestern Nova Scotia
Bibliographic record
Abstract
The region we selected for this study comprises three counties, each named \nfor its main population centre: Digby, Yarmouth, and Shelburne. We subtracted \nQueens and Lunenburg counties from Statistics Canada’s economic region, as \nboth are too close to Halifax to meet our definition of a peripheral region. The \nregion selected is fairly symmetrical, with the small regional capital of Yarmouth \nin the middle, and Digby and Shelburne on either side. Southwestern Nova Scotia \nis essentially a rural region, and it is the only region in our research program that \nhas no census agglomeration. In addition, this region has the highest \nconcentration of francophones in Nova Scotia, in the rural municipalities of Clare \nand Argyle.Given the cycles typical of marine resource development, the economy of the Southwestern experiences the kinds of highs and \nlows that have shaped the history of the Atlantic provinces. During the 1990s, \nfor instance, the groundfish crisis that shook the whole Atlantic region did not \nspare Southwestern Nova Scotia, although some diversification in the species \ncaught, together with excellent lobster catches, afforded a degree of protection \nfrom the disaster.Southwestern Nova \nScotia remains a peripheral region from the structural standpoint. While its \nresource-based economy continues to flourish, it is not benefiting from the development of the knowledge-based economy, which remains essentially an urban \nphenomenon. The region is also heavily dependent on government transfer \npayments. Still, there are some promising development initiatives. In the Digby \narea, for example, a dynamic industrial park has successfully replaced the former \nCornwallis military base, while at the regional level, the two regional development agencies show unparalleled vitality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".