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Record W3125662612 · doi:10.5430/ijfr.v12n3p157

Strategies of Sustainable Cooperative Partnerships

2021· article· en· W3125662612 on OpenAlexvenueno aff
Misbahul Munir, Maretha Ika Prajawati, S Basir

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipBusinessLoanDemocracyDescriptive researchSustainable developmentFinanceFinancial servicesEconomic growthMarketingEconomicsPolitics

Abstract

fetched live from OpenAlex

Cooperatives also have an active role in efforts to enhance the life of the nation and to realize a national economy based on the principles of family and economic democracy. The problem faced by cooperatives today is that cooperatives are less able to become business institutions that provide good services to all members and society in general. Limited funds and low human resources become a barrier for cooperatives to develop. Partnerships between cooperatives and other financial institutions are alternatives to improve cooperatives in developing their businesses. This study purpose on developing a cooperative economic development through a strategic partnership approach between cooperatives and other financial institutions so that cooperatives can grow, develop, and be sustainable. The method used in this research is descriptive qualitative research using in depth interview with informant. The informant is a cooperative for savings and loan managers who are willing to be interviewed in Batu City. The results showed that the cooperative strategy in building partnerships between cooperatives and other financial institutions was carried out based on the principle of mutual benefit. Cooperatives need to improve themselves so that they can meet the feasible and bankable criteria. Thus partnerships between cooperatives and other financial institutions will be established, thus cooperatives will grow and develop.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.114
GPT teacher head0.369
Teacher spread0.255 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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