PROPOSAL FOR ESTABLISHING AN ENVIRONMENT, SOCIAL,AND GOVERNANCE (ESG) GROUNDWORK: CREATING ACLOSED SYSTEM WITHIN THE MICROFINANCE SECTOR
Bibliographic record
Abstract
The current microfinance industry is unprepared for Social Responsible Investment1, which it inevitably starts to attract on its course towards the status of mainstream asset class. Derived from unsolved inner philosophical tension, the gap between differently used concepts in the sector is widening, while identical terms indicate different contents. Microfinance is becoming too varied to be presented under few single terms with discrepant meanings. Development of the microfinance industry, predominantly a phenomenon of local markets, thus does not keep up its local pace with its increasing reliance on international capital markets, with their globally coherent corporate expectations. The lack of clear definitions and transparency of the sector can discredit microfinance, once SRI systems open their gates. MIVs2, States, corporate investors and multilateral institutions must therefore in a concerted action impose basis of 1 Sustainable and Responsible Investment concept incorporates Environmental, Social and Governance issues into fund management. 2 MIV: microfinance investment vehicle standards coordinates, otherwise differently perceived concepts might mislead the global public. Therefore, formation of a closed system, ruled by Global Microfinance Authority using own currency unit and complemented with an ombudsman, comparable to United Nations in world of politics yet applied in microfinance market and financed by the contributing players, could impose globally understandable structure in local chaotic environment.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".