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Record W2084860851 · doi:10.1177/1077800407304456

Ethics Review for Qualitative Inquiry

2007· article· en· W2084860851 on OpenAlexaffabout
Kate Connolly, Adela Reid

Bibliographic record

VenueQualitative Inquiry · 2007
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsResearch ethicsInstitutional review boardEngineering ethicsQualitative researchAuditSociologyInstitutionEthical codePsychologySocial scienceManagementEngineering

Abstract

fetched live from OpenAlex

In many institutions, the institutional review board/research ethics board (IRB/REB) uses the traditional audit approach that emerged from the biomedical community (e.g., Nuremburg Code, Belmont Report) to review the ethical acceptability of research using humans as participants. This approach is guided by participant protection and risk management concerns. This article discusses the approach to ethics review currently being adopted at a large Canadian university in transition from a teaching to a research institution. It articulates the values that guide the REB in its deliberations and explores how these values support a facilitative—rather than an audit—approach to ethics review. Two case studies of innovative qualitative inquiry are discussed to demonstrate how Concordia University (Montreal, Canada) has adopted an approach to ethics review that attempts to encourage, engage, and support qualitative researchers in their research initiatives, while respecting established legal and ethical guidelines targeted primarily at clinical research.

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

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
Theoretical or conceptualmedium
gptMetaresearchResearch integrity
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
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)0.124
metaresearch head score (Gemma)0.272
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1240.272
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.005
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 head0.904
GPT teacher head0.772
Teacher spread0.132 · 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

Labeled directly by 2 models reading the full record.

MetaresearchResearch integrity

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
DomainMethods
GenreEmpirical · Methods

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

Citations25
Published2007
Admission routes2
Has abstractyes

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