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The New Corporate Social Responsibility

2008· article· en· W2157241724 on OpenAlexaff
Graeme Auld, Benjamin Cashore

Bibliographic record

VenueAnnual Review of Environment and Resources · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransformative learningCorporate social responsibilityConflationStakeholderStakeholder engagementPolitical sciencePublic relationsBusinessSociologyEpistemology

Abstract

fetched live from OpenAlex

The last half decade has witnessed a remarkable resurgence of attention among practitioners and scholars to understanding the ability of corporate social responsibility (CSR) to address environmental and social problems. Although significant advances have been made, assessing the forms, types, and impacts on intended objectives is impeded by the conflation of distinct phenomena, which has created misunderstandings about why firms support CSR, and the implications of this support, or lack thereof, for the potential effectiveness of innovative policy options. As a corrective, we offer seven categories that distinguish efforts promoting learning and stakeholder engagement from those requiring direct on-the-ground behavior changes. Better accounting for these differences is critical for promoting a research agenda that focuses on the evolutionary nature of CSR innovations, including whether specific forms are likely to yield marginal or transformative results.

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)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0040.023
Scholarly communication0.0160.015
Open science0.0010.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations324
Published2008
Admission routes1
Has abstractyes

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