MétaCan
Menu
Back to cohort
Record W2606818799 · doi:10.1504/ijbge.2017.10004554

Indias mandatory CSR policy: implications and implementation challenges

2017· article· en· W2606818799 on OpenAlexaff
Priya Nair Rajeev, Suresh Kalagnanam

Bibliographic record

VenueInternational Journal of Business Governance and Ethics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCorporate social responsibilityBusinessCompanies ActAccountingPublic relationsSection (typography)Diversity (politics)Corporate governanceFinancePolitical scienceCorporate lawLaw

Abstract

fetched live from OpenAlex

The increasing emphasis on social responsibility across the world is not new (Warhurst, 2005) and many countries require companies to disclose information about their environmental, social and employee-related impact, as well as their diversity policy (The Hauser Institute, 2015). India took CSR to the next level by mandating it for all companies through the recently introduced Section 135 in the Companies Act (2013). The provisions of the section require companies to establish a CSR committee consisting of three members of the Board of Directors to develop a CSR policy and review the CSR activities and prepare periodic reports. The above mentioned CSR infrastructure therefore necessitates significant capacity building within companies. With respect to implementation, companies may channel their resources through qualified nongovernmental organisations (NGOs). Consequently NGOs will also require significant capacity building. In this paper we identify the implications of the new guidelines that are worthy of consideration; these implications are for companies that meet the criteria to and therefore must comply with the provisions contained in Section 135, the organisations (including NGOs) that will implement the activities and other general implications. Furthermore the paper suggests mechanisms by which several of these challenges can be met and managed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.502

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 head0.066
GPT teacher head0.353
Teacher spread0.287 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Business Governance and EthicsSame topicInnovation and Socioeconomic DevelopmentFrench-language works237,207