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Record W2036421953 · doi:10.5153/sro.1970

Blurring Public and Private Sociology: Challenging an Artificial Division

2009· article· en· W2036421953 on OpenAlexaff
Kate Butler

Bibliographic record

VenueSociological Research Online · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicContemporary Sociological Theory and Practice
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSociologyPublic sociologySociology of leisureHistorical sociologyGovernmentalityArgument (complex analysis)EpistemologySociology of disasterMedical sociologySociology of EducationField (mathematics)ModernitySociology of lawSocial scienceConversationLawPoliticsPolitical science

Abstract

fetched live from OpenAlex

This article encourages sociologists to take a hybrid approach to the incorporation of public sociology into the discipline. The idea of public sociology rests upon a double conversation between sociologists as public actors, and the involvement of the ‘extra-academic’ world into the dialogue. However, the separation of public sociology from professional sociology is artificial. The division of labour between those working solely in academia, and those reaching out to the public at large is imaginary: sociologists do work in both the public and private. By blurring the line between public sociology and professional sociology (which constitutes a ‘privacy’ of sorts), sociology is able to reach a larger audience. To illustrate this argument, I examine how three theoretical approaches within sociology, governmentality literature, critical realism and second modernity, exemplify both public and private sociology, while remaining methodologically coherent and rigorous. These approaches show sociology to be a field in which disparate, multiple, fluid theories and metatheories exist side-by-side in work that is both public and professional.

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)0.047
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.047
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.002
Science and technology studies0.0140.151
Scholarly communication0.0290.046
Open science0.0030.029
Research integrity0.0100.015
Insufficient payload (model declined to judge)0.0040.001

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 head0.510
GPT teacher head0.544
Teacher spread0.034 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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