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Record W3121804783 · doi:10.22215/etd/2016-11686

Competition or Liberation? Implications of Labour Automation in the Context of Cognitive Capitalism

2016· dissertation· en· W3121804783 on OpenAlexaff
Russell Burgess

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnergy
TopicEnergy, Economy, and Technology Trends
Canadian institutionsCarleton University
Fundersnot available
KeywordsCapitalismContext (archaeology)Competition (biology)WageWork (physics)AutomationConstraint (computer-aided design)EconomicsEconomic systemPolitical economyLabour economicsPolitical scienceEngineeringPoliticsLawMechanical engineering

Abstract

fetched live from OpenAlex

Technology in the first half of 21st century are developing new abilities to perform autonomously and compete with humans directly in more and more tasks, opening up the future possibility of increasing labour substitution. Using the theory of Cognitive Capitalism to examine advanced economies as the most recent form of capitalism shows that in the modern economy work is increasingly central to the lives of individuals due to new cognitive labour which requires more worker engagement than industrial labour. This requirement has strengthened the direct coercive mechanisms of the increasingly precarious wage relationship and weakened alternate income sources. This dissertation argues that automation in this context could be harmful to individuals required to depend on work to survive and evaluates three policy options against the goal of freeing individuals from this institutional constraint to work so that they can continue to fully and freely participate in society if widespread automation occurs.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.044
Scholarly communication0.0070.008
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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