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Crown corporations and co‐operatives as coping mechanisms in regional economic development

2005· article· en· W2022528814 on OpenAlexaffvenueabout
Murray D. Rice, Darren C. Lavoie

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsCoping (psychology)Perspective (graphical)BusinessCrown (dentistry)Economic geographyEconomic growthEconomicsPsychology

Abstract

fetched live from OpenAlex

This paper focuses attention on two types of businesses, Crown corporations and co‐operatives, that have long been associated with attempted solutions to regional economic developmental problems in Canada. The paper argues that co‐operatives and Crown corporations can be viewed as coping mechanisms that attempt to make up for shortcomings in Canada's market‐based economic system. Consistent with this perspective, the case study of co‐operatives and Crown corporations finds that, taken as a single group, these firms are more spatially dispersed than their privately held and publicly traded counterparts at both the Canadian national level and the regional level in Saskatchewan. The study also shows that, taken separately, Crown corporations are highly concentrated within Saskatchewan, while co‐operatives are dispersed across the province. A possible explanation for this behaviour, warranting further research, is that Crown corporations in Saskatchewan encourage development provincially by linking with global and national business networks in their respective industries, while co‐operatives in Saskatchewan largely focus on facilitating economic development opportunities at a local level across the many smaller town‐ and city‐centred regions of the province. The paper discusses the meaning of these and other findings for regional economic development efforts in Saskatchewan and Canada.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.197
Teacher spread0.183 · 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 designNot applicable
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

Citations8
Published2005
Admission routes3
Has abstractyes

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