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Record W4392570067 · doi:10.61869/qrzs8772

Italian community co-operatives and their agency role in sustainable community development.

2022· article· en· W4392570067 on OpenAlexfundno aff
Michele Bianchi

Bibliographic record

VenueJournal of Co-operative Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
FundersUniversità degli Studi di ParmaUniversità degli Studi di TrentoUniversity of TorontoGlasgow Caledonian University
KeywordsAgency (philosophy)Community developmentBusinessSustainable developmentSustainable communityEnvironmental planningEconomic growthEnvironmental resource managementPolitical scienceSociologyGeographyEconomicsSocial science

Abstract

fetched live from OpenAlex

Italian community co-operatives are the most recent evolution of the Italian co-operative movement. They operate to carry out community development processes, which involve the local population in the re-thinking of socio-economic models of local development. They also create business opportunities using local resources and assets with particular attention to cultural aspects, local environments, and people’s needs. Generally, these co-operatives expand the mutualistic benefits — typically shared among co-operative members — with other community members because of the common belonging to the same place. Therefore, community co-operatives develop “community economies” for the general interest. What is less known about this phenomenon is whether and how community co-operatives consider the sustainability of their missions and activities. Drawing on sustainable community development theory, this paper reports on a comparative study of 17 Italian community co-operatives based on semi-structured interviews with representatives. Findings show how members use the co-operatives as an agency to foster sustainable development in their communities.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.296
Teacher spread0.251 · 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 designQualitative
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

Citations4
Published2022
Admission routes1
Has abstractyes

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