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Record W4281889584 · doi:10.1787/480a47fd-en

Legal frameworks for the social and solidarity economy

2022· report· en· W4281889584 on OpenAlexaff

Bibliographic record

VenueOECD local economic and employment development (LEED) working papers · 2022
Typereport
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité du Québec à Montréal
FundersEuropean Commission
KeywordsSolidarity economySolidaritySocial economyEuropean unionSocial solidarityField (mathematics)Economic systemRelevance (law)Political scienceDiversity (politics)EconomySociologyBusinessEconomicsSocial scienceLawEconomic policy

Abstract

fetched live from OpenAlex

The OECD Global Action “Promoting Social and Solidarity Economy Ecosystems”, funded by the European Union, through its work stream on legal frameworks, endeavours to: 1) increase knowledge and understanding on legal frameworks for the social and solidarity economy; 2) explore approaches and trends of legal frameworks to regulate the social and solidarity economy as a whole and social economy organisations; and 3) understand how legal frameworks can be used to promote and develop the social and solidarity economy in different contexts. This paper defines the legal notions, traditions and approaches to better understand legal frameworks that regulate the field. It presents and analyses the diversity, relevance and implications of legal frameworks that regulate the social economy; takes stock of the processes that lead to their design and implementation; identifies possible criteria for assessing their performance; and highlights the crosscutting issues and policy examples that could inspire countries.

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)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.032
Scholarly communication0.0120.009
Open science0.0020.008
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0160.002

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 head0.320
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations41
Published2022
Admission routes1
Has abstractyes

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