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Record W4312080335 · doi:10.1177/05694345221145008

Conjuring-Up a Bad Guy: The Academy’s Straw-Manning of Milton Friedman’s Perspective of Corporate Social Responsibility and its Consequences

2022· article· en· W4312080335 on OpenAlexaff
Jeff Muldoon, Anthony M. Gould, Derek K. Yonai

Bibliographic record

VenueThe American Economist · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCorporate social responsibilityMainstreamOpposition (politics)Shareholder valueEconomicsPerspective (graphical)Value (mathematics)Social responsibilityShareholderTheme (computing)Positive economicsBusiness ethicsSociologyLaw and economicsPublic relationsManagementLawPolitical sciencePoliticsCorporate governance

Abstract

fetched live from OpenAlex

Corporate Social Responsibility (CSR), the idea that business stewards have a broader range of societal obligations than maximizing shareholder value, is a mainstream theme in contemporary management research, education, and practice. Carroll points to one of its controversial aspects when he describes a clash between management scholars (who are generally pro-CSR) and their neoclassical economic contrarians. This has become an increasingly one-sided conflict with the pro-CSR side prevailing in both business and academia. CSR proponents have generally viewed Milton Friedman as an opponent of CSR. However, we argue that Friedman’s purported opposition to CSR is something of a caricature. We reveal that Friedman was concerned that his advocacy for free market operation would promote pro-social outcomes. Indeed, through his emphasis on creating value for consumers, being good stewards of scarce resources, and avoiding rent-seeking’s inefficiencies, Friedman has more in common with CSR proponents than is sometimes acknowledged.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.569

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 studies0.0010.001
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.260
Teacher spread0.222 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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