ENVIRONMENTAL ASSESSMENT IN MULTILATERAL DEVELOPMENT BANK INTERMEDIARY LENDING
Bibliographic record
Abstract
A substantial portion of private sector investments in emerging market economies internationally is routed through the use of Financial Intermediaries (FIs). FIs act as important gateways for channeling the resources from large Multilateral Development Banks (MDBs), to micro, small and medium-sized (SME) projects and enterprises whose comparatively limited business portfolios would otherwise make them ineligible for funding. During a MDB's scoping, FI clients are classified into a unique Category FI, whereby the onus for Environmental Assessment (EA) is transferred from the MDB to the FI. Although EA guidelines exist, FI institutions often fail to adequately incorporate them in their sub-project review. This increases the potential for environmentally and socially harmful development decisions being made by the FI with financial resources originating from MDBs. This paper identifies the factors limiting the successful incorporation of EA in FI subproject financing, in an attempt to develop tools to assist MDB's and their FIs to attain compliance with local, national and international EA laws and regulations.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".