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Record W3026953935 · doi:10.1177/0169796x20924365

States and Firms Co-producing Corporate Social Responsibility (CSR) in the Developing World

2020· article· en· W3026953935 on OpenAlexaff
Paul Alexander Haslam

Bibliographic record

VenueJournal of Developing Societies · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCorporate social responsibilityTypologyGeneral partnershipBusinessSalience (neuroscience)EnforcementProduction (economics)PoliticsPublic economicsEconomicsAccountingPublic relationsSociologyPolitical scienceFinanceMicroeconomicsLaw

Abstract

fetched live from OpenAlex

This article examines policy options that are co-produced by both states and firms, with the purpose of regulating an area of public policy and the practice of corporate social responsibility (CSR) by companies. The contributions of this article are twofold. First, it creates a typology of the co-production of corporate social responsibility, adding “delegated,” “brokered,” and “partnership” as intermediate categories between the natural end points of “voluntary” and “regulated.” Second, it proposes a framework for understanding why governments opt for a particular version of co-produced regulation, by focusing on the interaction between two key variables, the “net enforcement cost” and the “political salience of the demand for regulation.” The framework is tested on examples of the co-production of CSR from Argentina and Peru, where I identify pathways of change from one category of co-production to another.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.010
Scholarly communication0.0050.004
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.261
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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