ESG Disclosure Based on Regulatory Landscape: An Enquiry Into the Stock Exchange Enlisted Banks in a Fastest Growing Economy
Bibliographic record
Abstract
This research aims to reveal the extent, modes, and trend of ESG disclosure by the banks in Bangladesh. Analyses involve annual reports of 28 banks from 2016 to 2022 through the content analysis method using a ‘checklist’ comprised of 143 ESG disclosure items compiled from several policy guidelines regarding environmental and social performance disclosure circulated by the central bank, Bangladesh Bank. The study finds a spontaneously increasing tendency for ESG disclosure during the study period. Banks disclose information in both financial and non-financial modes, along with necessary details. However, information non-disclosure results in 75.18%, 63.38%, and 67.40% of environmental, social, and governance information, including many important ESG aspects that should not be ignored. The study result represents an optimistic scenario of ESG disclosure; nonetheless, Bangladeshi banks are yet to develop ESG disclosure practices.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".