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Record W4410638454 · doi:10.61869/umjc4895

Contradictions in Canadian co-operation: A diffusion of innovation approach

2020· article· en· W4410638454 on OpenAlexaboutno aff
Mitch Diamantopoulos

Bibliographic record

VenueJournal of Co-operative Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsDiffusionInnovation diffusionBusinessEconomic geographyEconomicsPhysicsThermodynamicsMarketing

Abstract

fetched live from OpenAlex

This paper challenges functionalist notions of Canadian co-operation as a static, ahistorical, monolithic or unitary system, instead focusing on its inherent tensions. Drawing together literature on European and Canadian movement history, diffusion of innovations theory is used to first examine the model’s trans-Atlantic and cross-continental spread. European invention dates are compared to Canadian first-adoption dates, across a range of co-operative models. This situates the Canadian co-operative experience in a comparative, historical, and world-system context. It also illustrates the scope, timescale, and significance of the lag in mutualism’s trans-Atlantic diffusion. The analysis spotlights how its parent movements’ often conflicting traditions gave rise to internal contradictions, such as mutualism’s language-based, bi-national structure. Similarly, Canadian regionalism resulted in a three- wave expansion: across Eastern and Central Canada, in the foundation stage; across the Western frontier as the railway opened the West to settlement; and, most recently, across the North. Finally, the paper addresses settler co-operation’s colonial legacy and the challenge of reconciling with Indigenous communities. Canadian mutualism’s dependent, delayed, divided, and uneven development is thus placed in comparative and critical context

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.072
GPT teacher head0.310
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

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