Government policy towards Community Economic Development in Manitoba
Bibliographic record
Abstract
Community Economic Development (CED) is a strategy to revitalise a community's economic, social and cultural dimensions. It is meant to help ailing communities heal and grow through a process of community-directed analysis, planning and project implementation. Because CED is a non-market response to social and economic problems, it necessarily depends on financial and institutional support from the government. CED's success hinges on the amount and type of support received from the three levels of government. It is important, therefore to undertake a detailed analysis of government policy towards CED in order to determine if it is properly formed and directed. The literature review at the beginning of the thesis provides a theoretical background for CED, including a working definition, the political economy of CED and some ideas on the economic theory of CED. The literature review is followed by an examination of 40 years of government policy towards CED. The government has been involved in CED in Manitoba for the past 50 years. The considerable archival material that documents this relationship is examined. This analysis will trace the evolution of government policy up to the present. Both successful and not-so-successful programs will be analysed. The results of semi-structured interviews with a variety of key informants will reveal how government policy has significantly impacted CED and will offer recommendations for its improvement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".