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Record W4404943966 · doi:10.33423/jabe.v26i6.7387

ESG Disclosure Based on Regulatory Landscape: An Enquiry Into the Stock Exchange Enlisted Banks in a Fastest Growing Economy

2024· article· en· W4404943966 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Applied Business and Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSecurities Regulation and Market Practices
Canadian institutionsnot available
Fundersnot available
KeywordsStock exchangeStock (firearms)BusinessAccountingFinancial systemFinanceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.218
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it