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Record W2143559286 · doi:10.46743/2160-3715/2013.1534

Found Poems, Member Checking and Crises of Representation

2015· article· en· W2143559286 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

VenueThe Qualitative Report · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsPoetryFeelingCredibilityCoding (social sciences)Qualitative researchPsychologySocial psychologyQualitative propertyComputer scienceSociologyEpistemologyLiteratureArtSocial science

Abstract

fetched live from OpenAlex

In order to establish veracity , qualitative researchers frequently rely on member checks to insure credibility by giving participants opportunities to correct errors, challenge interpretations and assess results; however, member checks are not without drawbacks. This paper describes an innovative approach to conducting member checks. Six members of a learning organization participated in two group interviews to examine the use of poetry as a method to promote individual and organizational learning. Several weeks later, participants received a copy of their transcripts and were asked to create a “found poem” to reflect their thoughts and feelings about using poetry as a learning tool. There was resonance between the interview themes produced by traditional open coding methods and those using participant - created found poems. However, the found poems added an emotional depth and connect ion that was missing from the traditional approach of coding qualitative data. This suggests that participant - created found poems can provide an agentic alternative to the usual method of member checking and also expand the notions of aesthetic approaches within qualitative 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.

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
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models agreeAgreement 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)0.023
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.949
GPT teacher head0.802
Teacher spread0.147 · 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