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Record W4213421370 · doi:10.1177/16094069221076929

Advancing the Impact of Critical Qualitative Research on Policy, Practice, and Science

2022· article· en· W4213421370 on OpenAlexaffabout
James Shaw, Monica Gagnon, Andrea Carson, Denise Gastaldo, Brenda Gladstone, Fiona Webster, Joan M. Eakin

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsWestern UniversityDalhousie UniversityUniversity of Toronto
Fundersnot available
KeywordsMainstreamQualitative researchPositivismEngineering ethicsSociologyCritical realism (philosophy of perception)Perspective (graphical)Management scienceEpistemologyPolitical scienceSocial scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Discourses of research impact shape the ways in which critical qualitative research and researchers are evaluated in contemporary academic environments. Mainstream conceptualizations of research impact arise from a positivist perspective that challenges the aims and approaches of critical qualitative research. In this paper, we propose a framework for conceptualizing the impact of critical qualitative research on policy, practice, and science. After critiquing literature that presents mainstream views on research impact, we summarize a recent framework for conceptualizing the impact of critical research specifically. We then add to the Machen framework by highlighting the impacts of critical qualitative research on the institutions and practices of science. We provide examples of ways in which researchers at the Centre for Critical Qualitative Health Research at the University of Toronto have made contributions to the impact of critical qualitative research on science, and conclude by addressing implications of this framework for the ways in which critical qualitative researchers can plan and evidence the impact of their work.

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 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)0.436
metaresearch head score (Gemma)0.374
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4360.374
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.012
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.828
GPT teacher head0.844
Teacher spread0.015 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2022
Admission routes2
Has abstractyes

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