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Record W2896790999 · doi:10.1177/0170840618800107

Governing Death: Organizing End-of-life Situations

2018· article· en· W2896790999 on OpenAlexaff
Marie-Astrid Le Theule, Caroline Lambert, Jérémy Moralès

Bibliographic record

VenueOrganization Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFoucault, Power, and Ethics
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsBiopowerPower (physics)Meaning (existential)PoliticsEthnographySociologyGovernment (linguistics)Political scienceEpistemologyLawAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

This paper examines the organization of death. Through an ethnographic study, we examine how a geriatrics department guides the end of life. Drawing on Agamben, we show that organizations that are dedicated to life, but regularly confronted with death, develop dispositifs (mechanisms, technologies, practices and relationships) to turn biopolitics (power over life) into thanatopolitics (a regime of death). We also show how the inherently political meaning of life disrupts such government of death. The inclusion of political life in a regime of death disrupts organizational practices that find themselves facing fundamental questions of what makes a life worth living, who can decide not to prolong life, and based on which criteria.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.030
Scholarly communication0.0080.008
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.125
GPT teacher head0.399
Teacher spread0.274 · 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 designQualitative
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

Citations33
Published2018
Admission routes1
Has abstractyes

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