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Record W4411327448 · doi:10.1177/00323292251346266

The Anti-ESG Backlash and Asset Manager Capitalism

2025· article· en· W4411327448 on OpenAlexaff
Adam Harmes

Bibliographic record

VenuePolitics & Society · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsBacklashCapitalismBusinessAsset (computer security)Market economyFinanceBusiness administrationEconomicsPolitical scienceComputer scienceLawArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This article examines the growing backlash among conservative activists and politicians in the United States against the use of ESG (environmental, social, governance) investment criteria by large asset management companies. It also examines the theoretical implications of the backlash for the growing literature on asset manager capitalism that has often viewed their promotion of ESG—at least in the United States—as mainly performative. To explain why the backlash has been so intense, it argues that we need to reexamine the different forms of power exercised by the Big Three asset managers and why they came to be viewed as a significant threat by the fossil fuel industry. It further argues that, in examining the impact of these different forms of power, it is necessary to make a distinction between the origins of the backlash with the fossil fuel industry and its subsequent acceleration among conservative politicians and activists.

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)0.004
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.013
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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
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

Citations10
Published2025
Admission routes1
Has abstractyes

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