A Macro-level Approach to Examining Canada’s Primary Industry Towns in a Knowledge Economy
Bibliographic record
Abstract
Using a macro-level approach the purpose of this paper is to examine the economic well-being of primary industry communities as well as ascertain if they are adequately preparing to compete in today’s knowledge economy. Large samples of census subdivisions (CSDs) were used to compare primary industry municipalities with diversified municipalities across Canada from 1986 to 2001. In addition, towns that maintained a high percentage of employment in primary industry were compared to ones that diversified their employment base. It was found that diverse municipalities endured a greater era of development and are better positioned for success in the future. Unexpected were results from 1986 to 1996 when diversifying primary industry towns did not incur substantial gains compared to concentrating primary industry communities. From 1996 to 2001 though, it became advantageous for communities to strive towards a diverse economic base. A contributing factor to this phenomenon is the fact that knowledge jobs played a minimal role in diversifying communities from 1991 to 1996. However, from 1996 to 2001 diversifying primary industry towns substantially increased the percentage of jobs related to the knowledge economy. Correlating to this shift in employment structure were positive changes to the socio-economic make up of these towns. Additionally the size and geography of single industry towns are revealed as significant factors for the viability of single industry towns. Utilisant une approche de niveau marco, l’objectif de cet article est d’étudier la santé économique des communautés de l’industrie primaire, et de déterminer si elles sont adéquatement préparées pour faire concurrence dans la nouvelle économie d’aujourd’hui. De grands échantillons de subdivisions recensées (CSDs) ont été utilisés, pour comparer les communautés de l’industrie primaire avec des municipalités à économie diversifiée à travers le Canada, de 1986 à 2001. De plus, les villes ayant maintenu un pourcentage d’emploi élevé dans l’industrie primaire ont été comparées aux municipalités qui ont diversifié leur emploi pendant la même période. Il a été démontré que les municipalités présentant une économie diversifiée vivent un développement durable et sont mieux positionnées pour le succès dans le futur. L’imprévu était le résultat de 1986 à 1996, lorsque les villes ayant diversifié leur industrie primaire n’ont pas obtenu de gains substantiels, en comparaison aux municipalités qui ont concentré leur économie dans le secteur primaire. Cependant, de 1996 à 2001, il devint avantageux pour les communautés de se diriger vers une économie diversifiée. Un des facteur contribuant à ce phénomène est le fait que les emplois hautement spécialisés jouaient un rôle mineur dans les communautés à économie diversifiée de 1986 à 1996. Toutefois, de 1996 à 2001, les villes à économie primaire s’étant tournées vers une économie diversifiée ont augmenté, de manière substantielle, le pourcentage d’emploi relié à l’économie du savoir. Corrélé à ce changement dans la structure de l’emploi, ces villes ont aussi présenté une évolution socio-économique positive.
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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 | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".