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Record W2887650615 · doi:10.4018/ijavet.2018100101

From the Professoriat to the Precariat

2018· article· en· W2887650615 on OpenAlexaff
Howard A. Doughty

Bibliographic record

VenueInternational Journal of Adult Vocational Education and Technology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsSeneca Polytechnic
Fundersnot available
KeywordsProletariatCommunismCapitalismEmancipationSociologyWorking classRetrenchmentPoliticsClass conflictPolitical economyAlienationNeoliberalism (international relations)Political scienceLawPublic administration

Abstract

fetched live from OpenAlex

Social class lies at the core of much that Marx said about the “laws of history.” Class conflict was to be the means whereby capitalism would be overthrown, superseded by a revolutionary dictatorship of the proletariat and, subsequently, by a communist society in which alienation and exploitation would be replaced by emancipation and the full flowering of human potential as both individuals and a species. The capitalist system, however, has proven remarkably resilient and resourceful. The welfare state ameliorated extreme economic distress, popular culture sapped revolutionary energy, and “identity politics” fragmented political radicalism. Meanwhile, the definition of social class itself became problematic. A reorganized labor market produced divisions between the traditional working class and precarious workers and, in colleges and universities, the old “professoriat” was joined by a new “precariat” that now does over two-thirds of the teaching. This trend is part of the “corporatizing” of higher education and the “neoliberal” restructuring of work in late capitalism. Intellectuals, once the theoretical “vanguard of the proletariat,” are now practical leaders too. Educational worker militancy has implications for the academy and class tensions throughout society. It raises the question: Was Marx wrong, or has he just not yet been proven right?

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)0.002
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: none
Teacher disagreement score0.835
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.011
GPT teacher head0.356
Teacher spread0.345 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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