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Record W1550047242 · doi:10.29340/20.1033

Reducción del daño: una preocupación central para la antropología médica*

2006· article· es· W1550047242 on OpenAlexfundno aff
Mark Nichter

Bibliographic record

VenueAmericanae (AECID Library) · 2006
Typearticle
Languagees
FieldSocial Sciences
TopicPublic Health and Social Inequalities
Canadian institutionsnot available
FundersSouthern Medical AssociationMcMaster University
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

El tema de la reducción del daño se ubica dentro de un area temática más extensa de la antropología: la antropología de la vulnerabilidad, del riesgo y de la responsabilidad. Esta area temática abarca el estudio de la percepción común de la vulnerabilidad, de laproducción del conocimiento sobre riesgo, de las reacciones de la gente a la información sobre riesgo, de la política de la responsabilidad y de las prácticas asumidas para minimizar los riesgos en el presente y en el futuro. La reducción del daño es una expresión de control de la propia vida al tiempo que una forma de manipulación en un entorno económico político en el cual la industria de la reducción del daño está dispuesta a aprovechar las perspectivas cada vez más amplias del riesgo, de las ansiedades colectivas y de la necesidad de la gente de sentir que tiene control.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0150.001

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 head0.015
GPT teacher head0.323
Teacher spread0.308 · 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 designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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