MétaCan
Menu
Back to cohort
Record W2626024435 · doi:10.1177/1609406917711351

Negotiating the Complexities and Risks of Interdisciplinary Qualitative Research

2017· article· en· W2626024435 on OpenAlexafffund
Dawn E. Trussell, Stephanie Paterson, Shannon Hebblethwaite, Trisha Xing, Meredith Evans

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsConcordia UniversityBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNegotiationQualitative researchVulnerability (computing)Engineering ethicsLegitimacyDisciplineKnowledge productionSociologyPoliticsWork (physics)Political sciencePublic relationsKnowledge managementSocial scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

This article interrogates the experiences of an interdisciplinary research team that engaged in a qualitative research program for over 5 years, beginning with the grant writing process through to knowledge dissemination. We highlight the challenges of constructing shared understanding and developing research synergies, embracing vulnerability and discomfort to advance knowledge, and negotiating risks of legitimacy and transcending disciplinary boundaries. Based on critical reflections from the research team, the findings call attention to the politics of knowledge production, the internal and external obstacles, and the open mindedness and emotional sensitivity necessary for interdisciplinary qualitative research. Emphasis is placed on relational and structural processes and mechanisms to negotiate these challenges and the potential for interdisciplinary research to enhance the significance of scholarly 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 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)0.605
metaresearch head score (Gemma)0.484
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.395
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6050.484
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0090.006
Science and technology studies0.0450.098
Scholarly communication0.0370.030
Open science0.0090.060
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0030.001

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 head0.959
GPT teacher head0.831
Teacher spread0.128 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations33
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Qualitative MethodsSame topicInterdisciplinary Research and CollaborationFrench-language works237,207