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Record W4382067851 · doi:10.1177/00218863231183217

Collaborative Inquiry Fuelled by Reflexive Learning: Changing Change

2023· article· en· W4382067851 on OpenAlex
Elena P. Antonacopoulou, Regina F. Bento, Gareth Edward, Beverley Hawkins, Christian Moldjord, Clare Rigg, Chrysavgi Sklaveniti, Woon Gan Soh, Captain Christina Stokkeland

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

VenueThe Journal of Applied Behavioral Science · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsReflexivityProcess (computing)Action (physics)Action learningCollaborative learningSociologyEpistemologyKey (lock)Knowledge managementComputer sciencePedagogyCooperative learningTeaching methodSocial science

Abstract

fetched live from OpenAlex

In this paper, we dig deeper into the reflexive learning that fuels collaborative inquiry by examining the unique ways in which changing itself takes place. We draw on two examples of collaborative inquiry, offering autoethnographic insights from our own lived experiences of changing change. These insights are underpinned by reflexive learning which we capture in textual form to show how learning in collaborative inquiry involves “impacting with” rather than “impacting on.” Our analysis reveals that reflexivity is not a homogenous or static experience but consists of several dynamically changing entangled “dimensions” of practice. Through dimensions relating to the process, content, and impact of reflexive learning, collaborators can arrive at a “stance”—a fluid, loosely shared basis for action that enables organizational practices to be reconfigured or preserve key principles.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.310
Teacher spread0.255 · 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