Bibliographic record
Abstract
This paper verifies a hypothesis that CFIs(Cooperative Financial Institutions) contribute to the reduction of income inequality as they are originally established with the purpose of expanding financial access for poor families and firms. We presume that CFIs in Korea, such as credit unions and community credit cooperatives, had operated according to their original purpose until 1980s, with the result that they helped lots of poor people enhance their economic situation. However, since 1980 they have been engaging in financial activities resembling those of profit-oriented commercial financial institutions. Analysing quarterly financial data of credit unions and community credit cooperatives from the 4th quarter of 1993 to the 4th quarter of 2010, we find that overall there is a long-term negative relationship between the development of CFIs and income inequality represented by Gini coefficient. This confirms that the development of CFIs in Korea contributes to the reduction of income inequality, which is somewhat contrary to our initial expectation based on their history. Nevertheless, it may be necessary to search for more appropriate way to prompt the development of CFIs because they keep trying to resemble commercial financial institutions in their operation. We suggest that allowing the establishment of new financial cooperatives be an answer, which is only possible with the revision of the Basic Law on Cooperatives enacted in 2012.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".