An environmental scan on exploring new rural economic development frameworks : defining the success factors
Bibliographic record
Abstract
There is no single recipe for economic prosperity.This is true for all rural areas, whether new economy winners or not.Research relevant to rural development shows that many industrialized countries are effective in adapting to their current economic distress while others are not.This paper's purpose is to examine new rural economic development approaches and initiatives through an environmental scan of Canada and the United States.The study identifies factors contributing to the success of new rural development initiatives as well as barriers, issues, and challenges that rural communities face and how they may overcome them.The scan revealed that rural communities, facing a similar economic decline as Nova Scotia, have found effective approaches to not only handle the poor economic condition but in fact prosper in spite.The findings from this study suggest that the prospering rural communities, despite being unique in many variables, all share at a minimum at least four commonalities, an increase in innovation, investment in people, products and places, perseverance, and connections amongst people, institutions and places.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".