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Record W3083544141 · doi:10.5430/rwe.v11n5p48

Nature and Extent of Corporate Social Responsibility in the Indian Banking Sector

2020· article· en· W3083544141 on OpenAlexvenueno aff
Mohammad Saleh Miralam, Vikram Jeet

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessReputationSocial responsibilitySample (material)StakeholderAccountingWelfareEconomicsMarket economyPublic relationsManagementPolitical science

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR), are societal initiatives of an organization for the community welfare and development. The purpose of the present study is to highlight the corporate social responsibility disclosure of Indian banks for the financial year 2014-15 to 2016-17. The contribution of Indian banks in CSR initiatives has been observed in the form of development of the rural sector, the contribution in basic education, generating more employment opportunities, public healthcare, and sanitation, etc. CSR has been emerged as an important factor in facilitating sustainable growth and valuing the stakeholder and featured as a competitive edge in the banking sector over its rivals and improves the reputation. The results of the study highlighted the compounded CSR spent and sector-wise CSR contributions by the Indian banks during the period of study. A compounded downfall of 7.70 percent has been observed in the growth of the amount required to CSR spent. Simultaneously, the downfall of 8.39 percent in the actual CSR spent has also been recorded in the study. But on the other hand, the amount of CSR spent of all sample banks has been increased by a compound growth of 22.60 percent during the period of study. Besides, the growth analysis of “sector-wise” CSR spent reflected an increase in the compound growth of all sectors by 21.80 percent.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.133
GPT teacher head0.345
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes1
Has abstractyes

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