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Record W4378436138 · doi:10.1515/9780228002369-002

Changing Subjects of Action Research

2020· book-chapter· en· W4378436138 on OpenAlexaboutno aff
Dennis Sumara

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2020
Typebook-chapter
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAction (physics)PsychologyPhysics

Abstract

fetched live from OpenAlex

Changing Subjects of Action ResearchI realized in the midst of writing this foreword that I am completing my fortieth year in the field of education -ten years as a classroom teacher and thirty in higher education, the last ten as dean of education.Reading through the chapters in this volume has been both a reminder of the important ways that Canadians have advanced the field of educational action research and also of how we have provoked that field.As I and others have argued elsewhere (2001), the co-emergence of curriculum and nation has inspired in Canada an openness to bibliographic inclusion in both our theoretical and methodological work.One needs only to review the citation lists of the chapters in this volume to notice how this continues to be the case.While I am cautious about essentializing the identities and work of Canadian researchers, I also believe it is important to note how our ability to value difference and diversity in our approaches to research -including educational action research -has resulted in important changes in schools, universities, and communities in Canada and elsewhere.Of course, the strength of any approach to research is its particularity -that which distinguishes it from other forms.This requires not only technical knowledge about methodological procedures but also strong understanding of the theoretical and conceptual knowledge and assumptions guiding those processes.As important -and perhaps, one could argue, most important -is knowledge of the histories of the emergence and evolution of the research methods/approaches used, with particular attention to how they were used in the specific context of historical times and situations.Most impressive and important about educational action research in Canada over the past several xii

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)0.107
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.107
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0240.216
Scholarly communication0.0430.027
Open science0.0050.017
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0080.001

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 head0.269
GPT teacher head0.416
Teacher spread0.146 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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