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Record W2979317242 · doi:10.1177/1357034x19876967

The Biopolitical Embodiment of Work in the Era of Human Enhancement

2019· article· en· W2979317242 on OpenAlexaff
Nicolas Le Dévédec

Bibliographic record

VenueBody & Society · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsBiopowerHuman enhancementSociologyEmancipationPerspective (graphical)Work (physics)PhenomenonOccupational scienceEnvironmental ethicsSocialityAestheticsEngineering ethicsEpistemologyPsychologyGender studiesPolitical sciencePoliticsLawEngineeringComputer sciencePhilosophyEcologyMechanical engineering

Abstract

fetched live from OpenAlex

Human enhancement or the use of technoscientific and biomedical advances to improve human performance is a social phenomenon that has become increasingly significant in Western societies over the last 15 years or so, notably in the workplace. By focusing on the non-medical use of psychostimulants, and from a perspective that is both critical and exploratory, this article aims to show that human enhancement practices prefigure new forms of embodiment and interiorization of work that are contributing to a significant reconfiguration of biopower. By allowing individuals to technically push back their physical and mental limits, beyond what is considered ‘normal’, human enhancement is enabling a form of biopower that is focused on the individual and on the possibility of reconfiguring biological norms in themselves. Far from participating in workers’ emancipation, this biopolitical model of enhancement markedly points to the issues of intensifying work conditions and increased employee self-discipline.

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)0.006
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.995
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.071
Scholarly communication0.0080.007
Open science0.0010.009
Research integrity0.0020.004
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 head0.043
GPT teacher head0.337
Teacher spread0.294 · 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.

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
Published2019
Admission routes1
Has abstractyes

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