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Record W2231129175 · doi:10.11634/233028791503687

Democracy and Enterprise. A Philippine Cooperative Balances Social and Business Demands

2015· article· en· W2231129175 on OpenAlexaff
Angeline Lim, Nonita T. Yap, John F. Devlin

Bibliographic record

VenueAmerican Journal of Business and Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBusinessProfit (economics)GlobalizationMarket economyConsumption (sociology)EconomicsEconomic growthLabour economics

Abstract

fetched live from OpenAlex

A central concern of the social enterprise literature is the tension between an organization’s social and its business mission.  This paper argues that cooperatives avoid this tension because organizational decisions are made by the social beneficiaries – the cooperative members.   This is demonstrated with the experience of Sorosoro Ibaba Development Cooperative (SIDC) in the Philippines.  SIDC started in 1969 with 59 small farmers each contributing US$ 10.  SIDC now offers a range of social and economic services to nearly 18,000 members with assets reaching US$ 36 million in 2012.   However SIDC currently faces very important challenges, the most formidable of which is the increasingly globalised production and consumption system.  SIDC has adjusted to market pressures not by internationalising its markets, investment, management and resources  but through vertical integration of its domestic supply chain, adoption of technological innovations and by tapping migrant workers’ savings.  However, globalisation also means that SIDC products and services compete with those produced without concern for workers’ safety, local employment or environmental health.  The threat is exacerbated by trade agreements that erode state capacity to temper the corporate drive for profit maximisation with peoples’ right to employment, living wage, and a healthy environment

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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 designOther design
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

Citations0
Published2015
Admission routes1
Has abstractyes

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