Prosperity \nthrough \nCollaboration: \nReview \nof \nRegional \nDevelopment \nModels \nand \nPotential \nApplications \nto \nthe \nBurin \nPeninsula
Bibliographic record
Abstract
This paper identifies four regional development models from Canada, the United \nStates, and the European Union: Municipalité Régionale de Comté, Liason Entre \nActions de Développement l’Economie Rurale (Leader), Regional Competitiveness \nModel, and the Community Collaboration Model. Each model was selected \nbecause they have been deemed succesful in some regional development \nliterature and collectively represent a diverse collection of regional development \nmodels. In discussion with the Burin Peninsula Regional Council the list of regional \ndevelopment models was finalized. An overview of each model is providing, \nhighlighting key indicators for success, when available. Using the commentary \nreceived from community residents of the Burin Peninsula regarding \ncollaborations an initial statement of potential application is provided. This \nstatement should not be considered prescriptive; rather, an initial exploration. \nFurther discussions among community residents, community-serving \norganizations, governments, and businesses should be conducted to further \nexplore and validate the notions presented. \nAlthough a number of regional development models have been utilized \nthroughout rural communities in Canada and internationally, gathering evaluative \ninformation on these models can be challenging. In constructed this document, \nevaluative information about the models, assessments of critical success \nfactors, and lessons learned through the experience were not always publically \ndocumented. This lack of documentation hinders the ability to transfer \nknowledge and models to new rural regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.025 |
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".