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Record W2116918702 · doi:10.1177/1468794107076023

Learning in focus groups

2007· article· en· W2116918702 on OpenAlexaff
Victoria Wibeck, Madeleine Abrandt Dahlgren, Gunilla Öberg

Bibliographic record

VenueQualitative Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFocus groupModerationFocus (optics)Data collectionEmpirical researchPsychologyGroup (periodic table)SociologySocial psychologyEpistemologySocial science

Abstract

fetched live from OpenAlex

The focus group is a research methodology in which a small group of participants gathers to discuss a specified issue under the guidance of a moderator. The discussions are tape-recorded, transcribed and analysed. Notably, the interaction between focus group participants has seldom been evaluated, analysed or discussed in empirical research. We argue that considering the focus group in light of current research into interaction in problem-based learning (PBL) tutorial groups would facilitate the deliberate exploitation of group processes in designing focus groups, staging data collection and analysing and interpreting data. When the analytical focus shifts from mere content analysis to an analysis of what the participants themselves are trying to learn, one can explore not only what the participants are talking about, but also how they are trying to understand and conceptualise the issue under discussion.

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

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.071
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.071
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0060.007
Scholarly communication0.0060.008
Open science0.0030.013
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0240.007

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 head0.424
GPT teacher head0.666
Teacher spread0.242 · 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

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations290
Published2007
Admission routes1
Has abstractyes

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