The Economic Vitality of Small Cities in Canada: A Case Study of Kamloops and Prince George
Bibliographic record
Abstract
Economic Vitality Indicators s 1996 EconomicVitality Indicators 1 1. .Median income levels Median income levels -indicator of overall level of economic well being 2. 2. Employment rates Employment rates -indicator of overall level of employment 3 3. Quaternary employment Quaternary employment -indicator of high-tech employment intensity 4. 4. Population change Population change -indicator of new business creation 5. 5. Average value of dwelling Average value of dwelling -indicator of growth and prosperity 6. 6. Manufacturing employment levels Manufacturing employment levels -indicator of the intensity of the traditional industrial economy Literature Review Literature ReviewMany small cities and regional municipalities have sought to address demographic stagnation or decline by stimulating economic growth through a process of transitioning from a primary resource extraction base to an economy with secondary and quaternary oriented jobs (Nelson, 2005;Portnov and Wellar, 2004).For a number of small cities, success in expanding job opportunities came from a higher quality of life, innovations, and technology which is embedded within services and manufactured products and therefore, attracts and retains a skilled labour force (Siegle and Waxman 2001, p.32;Mackinnon and Nelson 2005;Cutler and Davies 2007;Bourne and Simmons 2003).Cities which have not yet transitioned from the "classic resource town" remain vulnerable to cycles of economic boom and bust (Nelson 2005, p.99).A host of problems follow a declining economy based on primary industry such as declining population, a shrinking tax base and fewer employment opportunities (Bourne and Simmons 2003; Siegle and Waxman 2001
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".