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Record W2127634162 · doi:10.22230/cjnser.2015v6n1a200

Social Enterprise in Atlantic Canada

2015· article· en· W2127634162 on OpenAlexaffvenueabout
Doug Lionais

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsCape Breton University
Fundersnot available
KeywordsTransformative learningSocial enterpriseEthnologySociologyHumanitiesPolitical science

Abstract

fetched live from OpenAlex

This article explores the history and experience of social enterprise within Atlantic Canada. As part of the International Comparative Social Enterprise Models (ICSEM) research project, this article aims to describe the unique historical, contextual, and conceptual approaches to social enterprise in Atlantic Canada. Four case studies are provided to illustrate the diversity of social enterprise in the region. The article argues that the historical roots of social enterprise in Atlantic Canada can be found within the Antigonish Movement, and that the founding political economic vision of that movement can inform a progressive and transformative approach to social enterprise in the region. Cet article explore l’histoire et la pratique de l’entreprise sociale dans les provinces de l’Atlantique. Écrit dans le cadre du projet ICSEM (« International Comparative Social Enterprise Models »), cet article a pour but de décrire les approches historiques, contextuelles et conceptuelles envers les entreprises sociales propres aux provinces de l’Atlantique. Il présente quatre études de cas afin d’illustrer la diversité des entreprises sociales de la région. L’article soutient que les racines historiques de l’entreprise sociale dans les provinces de l’Atlantique remontent jusqu’au Mouvement d’Antigonish, et que la vision politico-économique fondatrice de ce Mouvement pourrait sous-tendre une approche envers les entreprises sociales de la région qui soit progressiste et transformatrice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.144
GPT teacher head0.387
Teacher spread0.242 · 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 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

Citations9
Published2015
Admission routes3
Has abstractyes

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