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Record W3097475970 · doi:10.46743/2160-3715/2020.4239

Innovating the Study of Context: Using a Qualitative Study on Subjugation and Resistance to Explore the Utility of Foucauldian Governmentality as a Framework for Enriching Situational Analyses

2020· article· en· W3097475970 on OpenAlexaff
Hannah Kia, Carol Strıke, Daniel Grace, Lori E. Ross

Bibliographic record

VenueThe Qualitative Report · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsGovernmentalitySituational ethicsComplementarity (molecular biology)SociologyResistance (ecology)Context (archaeology)Qualitative researchSocial psychologySalientGrounded theoryEpistemologyPsychologySocial sciencePolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Situational analysis has, as an emerging poststructuralist approach to grounded theory, recently grown in use across a diverse range of disciplines and substantive areas. In this paper, we consider the complementarity of Foucauldian governmentality as a theoretical framework for supporting and enriching situational analyses. Our work is based on the findings of a recent study, informed by situational analysis, in which we interviewed 27 HIV-positive (n=16) and HIV-negative (n=11) gay men ages 50 and over about their health care experiences, and used these data to examine processes of subjugation and resistance reflected in their accounts. Drawing on our analytical process, we consider the utility of governmentality in identifying salient discursive forces within a situation of interest, in theorizing how contextual factors operate on and influence the experiences of key actors in a field of inquiry, and in generating insight on fluid uses of power within an area under examination.

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)0.049
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0130.046
Scholarly communication0.0100.015
Open science0.0020.011
Research integrity0.0020.003
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 head0.778
GPT teacher head0.700
Teacher spread0.079 · 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 designQualitative
DomainMethods
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
Published2020
Admission routes1
Has abstractyes

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