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Record W2338322115 · doi:10.1177/1541344616634889

Addressing Power in Conversation

2016· article· en· W2338322115 on OpenAlexaff
Liza Lorenzetti, Anna Azulai, Christine A. Walsh

Bibliographic record

VenueJournal of Transformative Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransformative learningReflexivityConversationParticipatory action researchSociologyCitizen journalismPower (physics)PsychologyAction researchAction (physics)PedagogyPolitical scienceSocial scienceCommunication

Abstract

fetched live from OpenAlex

The World Café (TWC), used as an effective conversational tool around the world, shares several tenets with other participatory approaches to learning and development. It has not been critiqued, however, for its insufficient attention to reflexivity, power differentials, and structural inequalities within its process, specifically in relation to TWC facilitators. As a group of women from diverse social locations and backgrounds committed to the pursuit of social justice, we sought this opportunity to explore and investigate the transformative learning capacities of TWC, with the broader goal of enhancing its usability in education and community settings. We reviewed and critiqued TWC conversation approach, suggesting stronger links to liberatory education and transformative learning theories. Using a participatory action research process leading to cocreated knowledge, we developed an Emancipatory Learning Charter, a new tool that can enhance the transformative learning potential of TWC.

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)0.041
metaresearch head score (Gemma)0.052
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 score0.041
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0160.038
Scholarly communication0.0140.023
Open science0.0030.023
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.043
GPT teacher head0.380
Teacher spread0.337 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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