MétaCan
Menu
Back to cohort
Record W3036285700 · doi:10.1111/apce.12279

Institutional and organizational trajectories in social economy enterprises: Resilience, transformation and regeneration

2020· article· en· W3036285700 on OpenAlexaff
Ignacio Bretos, Marie J. Bouchard, Alberto Zevi

Bibliographic record

VenueAnnals of Public and Cooperative Economics · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsResilience (materials science)Field (mathematics)Context (archaeology)Psychological resilienceRegeneration (biology)Corporate governanceOrganizational fieldDegeneration (medical)Organizational changePublic relationsBusinessSociologyEconomic systemPolitical scienceInstitutional theoryManagementEconomicsSocial sciencePsychology

Abstract

fetched live from OpenAlex

Abstract Social economy enterprises (SEEs) are arousing notable interest as promising alternatives where social innovation, business ownership and governance are concerned. In this context, a renewed debate about the internal changes and organizational trajectories experienced by these organizations has emerged in the scholarly literature. This special issue aims to contribute to ongoing debates in this field by advancing our understanding of the external pressures and internal dynamics that can trigger degeneration in SEEs, the conditions and factors that allow SEEs preserving their hallmark values and practices, and the resources and processes of organizational change that SEEs can deploy to overcome degeneration and regenerate. In this editorial introduction we introduce the topic of research, describe the content of the current special issue, and highlight some conclusions and possible directions for future research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.008
Scholarly communication0.0100.007
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.238
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Public and Cooperative EconomicsSame topicSharing Economy and PlatformsFrench-language works237,207