MétaCan
Menu
Back to cohort
Record W3152994287

La diversification économique des villes canadiennes non-métropolitaines entre 1971 et 2016

2020· article· fr· W3152994287 on OpenAlexaboutno aff
James Burnett

Bibliographic record

VenueEspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique) · 2020
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceGeographyArt
DOInot available

Abstract

fetched live from OpenAlex

La diversification économique fait l’objet d’un intérêt renouvelé depuis la Grande Récession de 2008. Supposée de renforcer la résilience des économies régionales face aux crises, la diversification est poursuivie comme objectif politique par de nombreux acteurs. Les régions non-métropolitaines, qui font face aux défis séculaires liés à la dépendance mono-industrielle, y voient une urgence particulière. Si des travaux précédents ont démontré que la diversification a bien eu lieu dans ces régions—en fait de manière accélérée relativement aux centres métropolitains—ce phénomène reste peu exploré. Afin d’éclairer ces enjeux, deux études sont proposées. Premièrement, une analyse est effectuée de la composition sectorielle de la diversification déjà observée dans les régions urbaines non-métropolitaines canadiennes, à travers une longue période de 1971 à 2016. Cette analyse a permis de constater qu’une bonne partie de la « diversification » observée dans ces régions est un artéfact statistique du déclin des industries antérieurement dominantes, comme celles du papier et de l’aluminium. Deuxièmement, des modèles de régression sont construits afin d’étudier la contribution de différentes variables régionales—la composition industrielle initiale, la diversité reliée et non-reliée, et l’accès à d’autres marchés—au développement de nouvelles industries et spécialisations menant à la diversification. Les résultats de cette analyse ont réaffirmé le rôle de l’accès aux marchés pour le développement régional, mais aussi de la composition industrielle initiale locale, ce qui réitère l’importance de prendre en compte celle-ci dans l’élaboration des politiques de développement régional qui correspondent aux conditions locales. <br /><br /> Economic diversification has experienced renewed interest since the Great Recession of 2008. Believed to strengthen the resilience of regional economies when faced with crises, various actors have pursued diversification as a policy goal. Non-metropolitan regions, confronted with challenges of secular decline resulting from dependence on a single industry, are particularly concerned. While previous works have shown that diversification has indeed occurred in these regions—in fact, more quickly than in metropolitan centres—the phenomenon remains little studied. In order to clarify these issues, two studies are proposed. Firstly, an analysis is conducted of the sectoral composition of the diversification already observed in Canadian non-metropolitan regions, throughout the period from 1971 to 2016. This analysis demonstrated that much of the “diversification” previously observed in these regions is a statistical artefact of the decline of previously dominant industries, such as paper and aluminum manufacturing. Secondly, regression models were constructed to study the contribution of different regional variables—initial industrial composition, related and unrelated diversity, access to the North American market—to the development of new industries and specializations which contribute to diversification. The results of this analysis reaffirmed the importance of market access for regional development, but also of local initial industrial composition. This serves to reiterate the importance of the latter when creating regional development policies appropriate to local conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.128
GPT teacher head0.294
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)Same topicRegional resilience and developmentFrench-language works237,207