Informing community planning for the knowledge-based economy : a case study of Manitoba's rural bilingual municipalities
Bibliographic record
Abstract
Communities must also work to ensure regional and community cooperation in economic development. I lntroductionThis research is about the way that planning has addressed economic opportunities for a group of rural, bilingual municipalities in Manitoba in the knowledge-based economy (KBE).Particularly in the current economic downturn Canada is facing, the government should be strategically investing in measures that will assist the economy in growth.Spending money on the future has been the key to successful economic growth in the past, according to Richard Florida, respected expert on the Creative Class and the knowledge-based economy.The familiar kind of stimulusthe "shovel-ready" kind that built highways and roads, and worked so well during the Great Depression ánd its aftermathworked precisely because it didn't stimulate that period's aging agriculture economy.lnstead, it accelerated the transition to a new economy based on housing, autos and all the products of the industrial assembly line, from refrigerators and washing machines to air conditioners and television sets.(Globe and Mail, 2009) Chapter 5 -Conclusions The conclusions are made, along wíth recommendations for community development corporations (cDCs), regional economic development organízations, as well as provincial and federal governments.Finally, suggestions are made for areas of further study on this topic.
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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 | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".