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Record W2610235217 · doi:10.1177/1476750317699461

Communicative space and the emancipatory interests of action research

2017· article· en· W2610235217 on OpenAlexaff
Paul Kolenick

Bibliographic record

VenueAction Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCommunicative actionConversationAction researchAction (physics)Space (punctuation)SociologyEpistemologyCritical theoryPedagogySocial scienceLinguisticsCommunicationPhilosophy

Abstract

fetched live from OpenAlex

The theory and practice of communicative space is explored through three select action research studies from a past edition of Action Research, that focus on the practice of “opening communicative space” in action research. In an examination of these studies, a story is told of how the emancipatory interests of the marginalized (i.e. African-American students in public schools, the Aged in healthcare, and the Roma people of northeastern Hungary) can be realized through the opening and emergence of communicative spaces throughout the action research process. This story is told through successive themes that include the expectations and interests of action researchers, the challenges that they encountered once the action research process was underway, and finally, their reflective observations upon new communicative spaces that had emerged. While theoretical perspectives on communicative space are considered, such as the social theory of Jürgen Habermas, this article looks especially to the practical framework of William Isaacs on “Fields of Conversation” to understand how the opening of communicative spaces contributes to the emancipatory interests of action research.

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)0.121
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.976
Threshold uncertainty score0.641

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.115
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0240.155
Scholarly communication0.0320.035
Open science0.0030.039
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.980
GPT teacher head0.843
Teacher spread0.136 · 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.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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