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African Economic Integration Initiatives and the Challenge of Responsible Business Conducts: Analysis of Corporate Social Responsibility Provisions in African Regional Trade and Investment Agreements

2023· article· en· W4389797359 on OpenAlexvenueno aff
Jean Bertrand Azapmo

Bibliographic record

VenueInterventions économiques · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityNexus (standard)Investment (military)BusinessContext (archaeology)Language changeEconomic integrationFree tradeSocial responsibilityInternational tradeEconomicsEconomic systemPolitical sciencePublic relationsPolitics

Abstract

fetched live from OpenAlex

Left alone, economic integration initiatives, which aim to promote growth through investment and trade liberalization, do not automatically generate win-win outcomes for all stakeholders, or lead to the inclusive economic growth and sustainable development of participating countries. This situation which is due among others to possible market failures and externalities of corporations’ activities, has increasingly become a matter of concern with the numerous corruption scandals; human rights violations and environmental degradation involving corporations observed in a recent past. How therefore to continue promoting economic integration while ensuring socially responsible conducts from businesses in societies where they operate? One approach that has recently gained traction is the institutionalization of corporate social responsibility (CSR) clauses in trade and investment agreements. This paper analyses 10 African regional trade and investment agreements concluded between 2000 and 2020 to determine the extent to which they converge with this trend and the approach adopted in regulating CSR. The research complements the literature on the nexus between international law and CSR in the African context.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.145
GPT teacher head0.330
Teacher spread0.185 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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