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Record W2772951174 · doi:10.1177/1476750317745955

Adaptation of a structured story-dialogue method for action research with social movement activists

2017· article· en· W2772951174 on OpenAlexaff
Blake Poland, Roxanne Cohen

Bibliographic record

VenueAction Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsYork UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsAction researchAction (physics)Movement (music)Qualitative researchSocial constructivismGrounded theoryAdaptation (eye)Participant observationContext (archaeology)Participatory action researchSocial researchSocial movementSociologyPedagogyPsychologySocial sciencePolitical scienceAestheticsPolitics

Abstract

fetched live from OpenAlex

Dialogue and story-telling are essential elements of many qualitative methodologies and action research itself, reflecting the constructivist paradigm in which qualitative research (QR) and action research (AR) are grounded, and the co-construction of knowledge that takes place amongst research participants (in group settings) and researchers. This paper reports on the adaptation of a structured story-dialogue method for research with social movement activists undertaken in the form of a series of regional weekend workshops animated by researchers and attended by Transition movement leaders and participants from multiple locales, as part of a larger study ( www.transitionemergingstudy.ca ). We draw upon participant observation, animator reflections, research team meetings, participant feedback, as well as workshop materials, in relation to two different adaptations of Labonte and Feather’s original formulation (1996) and subsequent reflections (2011), setting this in the context of a broader literature on structured story-dialogue methods with groups. The potential of structured story-dialogue methods for research on, with and for social movements is highlighted.

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
Qualitativehigh
gptMetaresearch
Domain: Methods · Genre: Methods
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 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)0.080
metaresearch head score (Gemma)0.063
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0040.009
Scholarly communication0.0050.006
Open science0.0040.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.925
GPT teacher head0.771
Teacher spread0.154 · 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.

Metaresearch

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

Study designQualitative
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

Citations8
Published2017
Admission routes1
Has abstractyes

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