Environmental compliance of BRAC microfinance enterprises: an assessment
Bibliographic record
Abstract
BRAG is concerned about environmental aspects in its development programmes. In \nthis backdrop, a cross-sectional comparative study was designed to assess \nenvironmental performance in BRAG microfinance enterprises. Total 1200 borrowers \n(579 Dabi, 621 Progoti) involved in various incomes generating activities were \nrandomly selected from 30 sadar upazilas (divisidnal capital of sub-districts). The \nanalysis was conducted on selected environmental indicators such as use of raw \nmaterials and chemicals, waste management, environmental pollution, knowledge \nabout key environmental aspects etc. Statistical techniques such as frequency \ndistribution, chi-square and t-tests were used to 'get proportion of values and to \ncompare the differences between indicator values. :The findings revealed that above \n90% respondents received information from BRAG staff about environmental pollution, \ntree plantation, waste management, use of safe water and latrine, keeping hand \nwashing materials, and so on. Use of alternative fue~ (79%) and raw materials (79.6%) \nwere relatively less received information from BRAG staff. Compared to the other \ntypes, mainly degradable raw materials were used jn both enterprises, while the use \nwas higher in Dabi (187.9%) than Progoti (161%). Thus, degradable wastes were \ngenerated higher in Dabi (213.8%) than Progotf (20?8%). Disposal of different types \nof wastes at fixed and safe place was found higher in Progoti than Qabi enterprises \n(e.g., degradable solid waste: Dabi 55.8%, Progoti 71%; non-degradable waste: Dabi \n34.8%, Progoti 45.8%; liquid waste: Dabi 39.6%, Progoti 52.2%). Probability of \nenvironmental pollution was observed less in both Dabi and Progoti enterprises. \nMajority of Oabi and Progoti enterprises used chemical materials in their business \nactivities, but adoption of safety measures was foond very less in both enterprises \nimplying exposure to health risks. Fixed and safe disposal of wastes and adoption of \nsafety measures during handling chemical materials are imperative to avoid \nenvironmental pollution and health risks.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".