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Record W2734617813

SUSTAINABILITY REPORTING IN FINANCIAL INSTITUTIONS: A STUDY OF THE NIGERIAN BANKING SECTOR

2017· article· en· W2734617813 on OpenAlexvenueno aff
Obiamaka Nwobu, Akintola Owolabi, F. O. Iyoha

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityTransparency (behavior)BusinessAccountingCorporate governanceSustainability reportingIndex (typography)Environmental Sustainability IndexFinanceSustainability organizationsCorporate social responsibilityCorporate sustainabilityPublic relations
DOInot available

Abstract

fetched live from OpenAlex

Transparency and disclosure practices of business organizations are key aspects of corporate governance. Business organizations are faced with the need to report on sustainability performance in economic, environmental and social terms. Financial institutions constitute providers of capital to other sectors of an economy. Thus, their sustainability performance is an important aspect of transparency and disclosures that should not be ignored. This study investigated sustainability reporting of Nigerian companies in the banking sector for the five-year period ended December 2014. A disclosure index was used to score the information content of corporate reports pertaining to sustainability indicators. There was an increase in the mean sustainability reporting scores of the banks across the five years. The economic indicators was skewed in favor of direct economic value generated, economic value distributed, estimated value of defined benefit plan obligations (liabilities). On the other hand, disclosures on climate change were few. Banks should focus on improving their environmental disclosures in areas of renewable materials used, greenhouse gas emissions and assessment of suppliers based on environmental risks.

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.

How this classification was reachedexpand

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)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.058
GPT teacher head0.313
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2017
Admission routes1
Has abstractyes

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