MétaCan
Menu
Back to cohort
Record W1973806513 · doi:10.1504/ijisd.2014.062853

US and UK social enterprise legislation: insights for China's social entrepreneurship movement

2014· article· en· W1973806513 on OpenAlexaff
Gil Lan

Bibliographic record

VenueInternational Journal of Innovation and Sustainable Development · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSocial entrepreneurshipChinaEntrepreneurshipLegislationCorporationPoliticsSocial enterpriseGovernment (linguistics)Context (archaeology)BusinessEconomic systemEconomic growthEconomicsPublic relationsPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

China's remarkable economic ascendancy has resulted in growing inequality, pollution and labour strife. This has led to an increasing interest in Chinese social entrepreneurship as one potential method of addressing these problems. However, social entrepreneurs in China face regulatory hurdles which drive them to register as commercial enterprises instead of as socially–minded non–government organisations. This can cause public and investor scepticism regarding whether these Chinese social entrepreneurs are truly social–mission driven. This suggests that China could benefit from legal structures that facilitate social enterprise such as community interest companies in the UK and benefit corporations in the USA. Drawing upon legal transplant and social entrepreneurship perspectives, this paper suggests that the UK community interest company model is more consistent with China's socio–political context than the US benefit corporation model and may form the basis for a dialogue regarding a future China–specific social enterprise corporate structure.

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)0.005
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0100.011
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0100.000

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Innovation and Sustainable DevelopmentSame topicCorporate Law and Human RightsFrench-language works237,207