The Power of Networks: Integration and Financial Cooperative Performance
Bibliographic record
Abstract
Abstract ** : The purpose of this paper is to perform a cross‐country survey of the level of integration of systems of financial cooperatives (FC) and its effect on measures of performance. We develop a classification scheme based on a theoretical framework that builds on published work using transaction cost economics (TCE) to explain integration of large numbers of financial cooperatives into networks. We identify three critical levels of increasing integration we call respectively atomized systems, consensual networks and strategic networks. Further, we test some of the propositions that result from the theoretical framework on an international sample of financial cooperative systems. Based on this analysis we can conclude that: (i) Integration is less (more) important in developing (more developed) countries and for very small (large) financial cooperatives as a determinant of efficiency. However, integration tends to reduce volatility of efficiency and performance regardless of development. (ii) Integration appears to help control measure of managers’ expense preferences that tend to affect performance of FC. (iii) Despite high costs of running hub‐like organizations in highly integrated system, these systems economize in bounded rationality and operate at lower costs than less integrated systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".