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Record W2102089655 · doi:10.1177/0032329214547351

Turning Labor into Capital

2014· article· en· W2102089655 on OpenAlexaboutno aff
Michael A. McCarthy

Bibliographic record

VenuePolitics & Society · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee Retirement Income Security ActPensionRestructuringEconomicsPoliticsControl (management)Investment (military)DemocracySolidarityCapital (architecture)FinanceShareholderCapital marketLawCorporate governancePolitical scienceManagement

Abstract

fetched live from OpenAlex

This article explores union attempts to control pension fund investment for the debate on financial restructuring in the United States. It puts popular control of finance into comparative and historical perspective and argues that laws and politics help explain why the flow of finance is corporate controlled. First, changes in the legal regime—the Taft-Hartley Act of 1947 and the Employee Retirement Income Security Act (ERISA) of 1974—put constraints on labor’s ability to influence investment decisions. This is evident when comparing single- and multi-employer plans, where the laws had different consequences. Second, attempts to reform these laws failed. Had they been successful, Carter’s proposed economic revitalization plan in the run-up to his failed reelection in 1980 would have created new ways for unions to control and redirect retirement investment for social purposes. The reform failure is treated as a “suppressed historical alternative” through a comparison with a successful reform in Quebec, Canada, which gave unions broad controls over the Solidarity Fund in 1983. The findings suggest, somewhat counter-intuitively, that legal restrictions need to be loosened for democratic control of finance to be possible. For pension funds, more regulations led to more corporate control, not less.

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)0.000
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 score0.869
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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