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Record W1954837135 · doi:10.1177/160940690900800307

Framing Experience: Concept Maps, Mind Maps, and Data Collection in Qualitative Research

2009· article· en· W1954837135 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

VenueInternational Journal of Qualitative Methods · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsData collectionFraming (construction)Data scienceComputer scienceQualitative researchQualitative propertyExploratory researchPerceptionPsychologySociologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Traditionally, qualitative data collection has focused on observation, interviews, and document or artifact review. Building on earlier work on concept mapping in the social sciences, the authors describe its use in an exploratory pilot study on the perceptions of four Canadians who worked abroad on a criminal justice reform project. Drawing on this study, the authors argue that traditional definitions of concept mapping should be expanded to include more flexible approaches to the collection of graphic representations of experience. In this way, user-generated maps can assist participants to better frame their experience and can help qualitative researchers in the design and development of additional data collection strategies. Whether one calls these data collection tools concept maps or mind maps, for a generation of visually oriented social science researchers they offer a graphic and participant-centric means to ground data within theory.

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)0.123
metaresearch head score (Gemma)0.069
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.135
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1230.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.966
GPT teacher head0.854
Teacher spread0.112 · 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