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Record W2262830731 · doi:10.1177/0020715215627190

Worker self-management in the Third World, 1952–1979

2016· article· en· W2262830731 on OpenAlexvenueno aff
Kristin Plys

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsUnrestGlobeState (computer science)Political economyMarxist philosophyPolitical scienceMarket economyEconomicsEconomic systemPoliticsLaw

Abstract

fetched live from OpenAlex

Worker self-management has proliferated at key historical moments, worldwide, since 1917. In the wake of decolonization and the national liberation movements of the mid-20th century, unprecedented levels were attained across the globe. By examining the major cases of worker self-management that began in 1952–1979 in the periphery and semi-periphery, I highlight the varied historical trajectories leading up to state suppression or absorption of worker self-managed firms. Management literature predicts that all states would respond more favourably to profitable rather than less profitable enterprises; Marxist approaches predict that socialist states would be more likely than capitalist states to favour workers’ control, and world-systems analysts would expect states in the semi-periphery to be more hospitable than states in the periphery to worker self-management. I show that none of these theoretical predictions are empirically sustained. Instead, I employ an inductive historical analysis and find that states are equally likely to terminate profitable and unprofitable enterprises, whether in socialist or capitalist states, and in periphery or semi-periphery. To explain this phenomenon, I propose an alternative theory – focused on social unrest and the balance of class forces – for states in the Third World having by and large called a halt to the experiment of worker self-management.

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 score0.881
Threshold uncertainty score0.340

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.382
Teacher spread0.342 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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