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Record W2310758420 · doi:10.1177/0741713616640881

Undressing Transformative Learning

2016· article· en· W2310758420 on OpenAlexaff
Lisa Quinn, A. John Sinclair

Bibliographic record

VenueAdult Education Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransformative learningAction (physics)PsychologyClothingAction learningQualitative propertyCognitive psychologySocial psychologyCooperative learningDevelopmental psychologyPedagogyTeaching methodComputer science

Abstract

fetched live from OpenAlex

Clothing is an integral part of our lives, yet modes of producing, using, and disposing of apparel have significant impacts on the environment. Our research explored the role transformative learning plays in the transition to more sustainable thinking and actions about clothing to illuminate instrumental learning processes and examine the relationship between instrumental and communicative learning. Using a qualitative case study approach, we gathered data on behaviors and attitudes ( n = 32), and examined in depth the learning participants underwent and the action they took ( n = 17). The data reveal that instrumental and communicative learning outcomes were plentiful, with participants discussing the array of skills, knowledge, and communicative insights they learned. Results indicate that instrumental learning makes action possible by allowing individuals to identify problems and solutions and to develop plans of action. Results also reveal the important interaction among instrumental and communicative learning as an individual seeks to understand an occurrence.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.026
Scholarly communication0.0080.008
Open science0.0010.017
Research integrity0.0020.004
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 head0.007
GPT teacher head0.306
Teacher spread0.299 · 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 designTheoretical or conceptual
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

Citations24
Published2016
Admission routes1
Has abstractyes

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