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Record W4385145676 · doi:10.1177/10778004231188048

Blurry Lines: Reflections on “Insider” Research

2023· article· en· W4385145676 on OpenAlex
Laura Yvonne Bulk, Bethan Collins

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

VenueQualitative Inquiry · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutoethnographyInsiderSituatedProcess (computing)SociologyPsychologyWork (physics)ReflexivityEpistemologySocial psychologyPublic relationsComputer sciencePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Insider research poses a range of benefits and challenges for researchers and the communities being researched. It is commonly advocated for disability research but there is limited work exploring disabled researchers’ experiences. Influenced by autoethnography and through a process of asynchronous structured conversations, we reflected on our experiences as two blind researchers. Through our collective reflective process and analysis, we created three main themes: insider research is complex and subjective, there is judgment about the “right” thing to do, and insider research requires “different” work. We argue that insiderness is more than sharing characteristics: it is a situated, fluctuating, and “felt” experience. The complexities, judgments, and emotional labor associated with insider research can challenge researchers in potentially very personal and unexpected ways. We propose that further investigation is required about how researchers can best prepare for, engage ethically throughout, and be supported through the insider research process.

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.

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)0.073
metaresearch head score (Gemma)0.049
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0730.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0030.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.929
GPT teacher head0.784
Teacher spread0.145 · 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