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Record W1988181312 · doi:10.1177/0306312712462461

The good, the bad and the perfect: Criticizing engagement practice

2012· article· en· W1988181312 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Studies of Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Alberta
FundersCopenhagen Business School
KeywordsCriticismDemocracySociologyPoliticsEpistemologyDichotomyThe artsProduct (mathematics)Public engagementPublic relationsPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Criticism seems to be a recurring and significant characteristic of public engagement exercises – as reflected both in general political discussion and in the academic literature on public engagement with science. This article suggests that rather than being a distraction from the main business of ‘technical democracy’, criticism lies at the heart of public engagement and in that way should be seen not simply as an unwelcome and unanticipated by-product but rather as a key constituent. Taking inspiration from previous science and technology studies’ treatments of ‘bottom line’ moves and also from Boltanski and Thévenot’s sociology of critical capacity, this article adopts an approach to radical critique that explores its ‘dynamic-yet-patterned’ character. Building upon a ‘translation’ model, but also a framework taken from the martial arts, a reconstruction is offered of one empirical study of lay membership on scientific advisory committees. Conclusions are drawn concerning not only the analysis of critical dialogue around engagement but also the implications for democratic practice.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptScience and technology studies
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0180.017
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.589
GPT teacher head0.554
Teacher spread0.035 · 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