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Record W2566010696 · doi:10.1037/qup0000064

An account from the inside: Examining the emotional impact of qualitative research through the lens of “insider” research.

2017· article· en· W2566010696 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

VenueQualitative Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsPublic Health Ontario
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsInsiderQualitative researchPsychologyEmpathySocial psychologyApplied psychologyPublic relationsSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The benefits and challenges of insider positionality have been much written about in relation to qualitative research. However, the specific emotional implications of insider research have been little explored. In this manuscript, I aim to bring the literature on insider positionality to the study of emotion in qualitative research through a reflection on my experiences as a "total insider" conducting interviews for a longitudinal qualitative study examining mental health during the transition to parenthood among sexual minority women. On the basis of this experience, I highlight emotion-related benefits and challenges of my insider positionality, as they pertain both to the quality of the research and to my personal experiences as a qualitative researcher. In particular, I examine the potential benefits of my insider positioning for establishing rapport and my capacity for empathy, and the personal emotional growth and learning that my insider positioning made possible for me. With respect to challenges, I examine how my emotional investment in the researcher-participant relationship influenced my role as a research instrument, and discuss the difficulties I encountered in managing appropriately boundaried relationships and making decisions about self-disclosure. I close by highlighting promising avenues for further exploration of the emotional implications of insider research, from the perspectives of both researchers and participants.

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
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
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)0.205
metaresearch head score (Gemma)0.083
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2050.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0070.045
Scholarly communication0.0000.001
Open science0.0040.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.915
GPT teacher head0.794
Teacher spread0.121 · 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