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Record W2994674754 · doi:10.1163/17087384-12340038

Sustainable Development and Corporate Social Responsibility under the 2018 Petroleum Host and Impacted Communities Development Trust Bill: Is Nigeria Rehashing Past Mistakes?

2019· article· en· W2994674754 on OpenAlexvenueno aff
Nojeem Amodu

Bibliographic record

VenueAfrican Journal of Legal Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilitySustainable developmentCorporate governanceGovernment (linguistics)BusinessCommunity developmentSocial responsibilityHuman rightsPublic relationsPublic administrationEconomicsEconomic growthPolitical scienceLawFinance

Abstract

fetched live from OpenAlex

Abstract The 2018 Petroleum Host and Impacted Communities Development Trust Bill before the Nigerian National Assembly was proposed to foster sustainable development (SD) and embed corporate social responsibility ( CSR ) in the oil and gas corporate activities within host communities. From the backdrop of SD and CSR as regulatory concepts, this article scrutinizes the Bill for its viability to realize its objectives in its current form. It raises concerns about: (i) perceived negligence by the government to provide social services and public goods, seeming to outsource such responsibilities to the business community; (ii) the reduction of CSR to capital or community development projects; and (iii) the absence of useful delimitation criteria to determine host and impacted communities. The article argues that past mistakes are being rehashed and queries the capacity of the Bill to live up to stakeholders’ expectations. Using the normative contributions of global templates such as the United Nations Guiding Principles on Business and Human Rights, the article recommends policy and regulatory changes to the Bill’s governance structure towards embedding effective CSR and engendering SD in the Nigerian oil and gas industry.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.243
Teacher spread0.196 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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