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Record W4319014140 · doi:10.1007/978-3-031-17403-2_11

Networking, Governance, and Stakeholder Engagement of Financial Cooperatives: Some National Case Studies

2023· book-chapter· en· W4319014140 on OpenAlexfundaboutno aff
Ermanno Tortia, Silvia Sacchetti

Bibliographic record

VenueHumanism in business series · 2023
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
FundersAtlantic Canada Opportunities AgencyFonds Wetenschappelijk OnderzoekUniversity of Galway
KeywordsGrassrootsCorporate governanceRelevance (law)BusinessStakeholderInstitutionFinancial servicesCartelFinancial institutionAccountingPublic relationsIndustrial organizationPolitical sciencePoliticsFinance

Abstract

fetched live from OpenAlex

We discuss the nature and relevance of networking among financial cooperatives (FCs) starting from the historical and institutional development of FCs in different countries, particularly in The Netherlands (Rabobank Netherlands), Canada (Desjardins Group), and Italy (Cassa Centrale Banca and ICCREA). There are important structural similarities in the evolution of FC networks in different countries, but they appeared diachronically and evolved in different patterns at different times. In particular: (i) the spontaneous emergence of FCs and their spontaneous tendency to create collaborative and non-competitive networks of actors and organizations with similar motivations and goals; (ii) the spontaneous and gradual evolution of networks from a consensual and informal form, which nonetheless registers high degrees of compliance, mutual support, and collective action, to more formalized forms that assume increasingly important strategic and supervisory functions; (iii) the creation of a central institution owned by the grassroots FCs that assumes the role of controller, lender of last resort, and service provider; (iv) the increasing concentration of the network through pronounced merger and acquisition (M&A) processes, and the development over time of unified and integrated governance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.125
GPT teacher head0.262
Teacher spread0.137 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2023
Admission routes2
Has abstractyes

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