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Record W2052860698 · doi:10.1177/1468794107082305

Negotiating the politics of identity in an interdisciplinary research team

2007· article· en· W2052860698 on OpenAlexaff
Lorelei Lingard, Catherine F. Schryer, Marlee M. Spafford, Sandra L. Campbell

Bibliographic record

VenueQualitative Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsNegotiationSociologyIdentity (music)PoliticsSocializationIdeologyQualitative researchNarrativePower (physics)Public relationsSocial sciencePolitical science

Abstract

fetched live from OpenAlex

This article explores the politics of identity in an interdisciplinary health research team that has been engaged in a qualitative research program for over five years. We draw on sociological theories of power and knowledge to explore our experiences of identity conflict, team socialization, and knowledge production. Structurally, our article integrates individual and group perspectives through personal narratives and collaborative critique as we explore the complex negotiations required to realize and maintain our team dynamic. These negotiations take place not only with one another as particularly positioned individuals, but also with the ideological and organizational forces that structure our scholarly worlds. We conclude with articulating `lessons learned' that we hope will enable other interdisciplinary research teams to realize the rich potential of their collaborative qualitative 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)0.125
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1250.091
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0370.072
Scholarly communication0.0280.019
Open science0.0030.025
Research integrity0.0060.007
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 head0.712
GPT teacher head0.786
Teacher spread0.074 · 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
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

Citations94
Published2007
Admission routes1
Has abstractyes

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