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Abortion discourse in Bolivian hospital contexts: doctors’ repertoire conflicts and the Saving Women device

2005· article· en· W1622881205 on OpenAlexaff
Susanna Rance

Bibliographic record

VenueSociology of Health & Illness · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsTrinity College
Fundersnot available
KeywordsAppropriationAbortionDeviance (statistics)NormativeSociologyDiscourse analysisCritical discourse analysisEmbeddednessGovernment (linguistics)Gender studiesPolitical scienceLawSocial scienceEpistemologyIdeology

Abstract

fetched live from OpenAlex

The author proposes going Beyond attitudes (Potter and Wetherell 1987) to a more nuanced assessment of doctors' discursive variations. Through an application of Gilbert and Mulkay's (1984) interpretative repertoires, she defined three voices--technical, normative and pragmatic--in which Bolivian doctors spoke of abortion. In State and social security hospital contexts, doctors hastened to express compliance with government policy and institutional norms regulating abortion and postabortion care. Technical and pragmatic considerations, however, often entered into conflict with established rules. When contradictions became apparent in their own discourse, doctors regularly drew on the Saving Women device. This accounting strategy enabled them to save face as up-to-date professionals through justifying temporary deviance from norms in terms of benefit to women treated. The author describes her development of the repertoires, their validation with different medical audiences, and doctors' critical appropriation of the model to explain their own discursive variations.

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)0.011
metaresearch head score (Gemma)0.014
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0160.053
Scholarly communication0.0110.007
Open science0.0020.012
Research integrity0.0030.004
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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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

Citations21
Published2005
Admission routes1
Has abstractyes

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