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Record W3208871041 · doi:10.33524/cjar.v22i1.571

Collaborative Action Research: An Inevitable Outcome?

2021· article· en· W3208871041 on OpenAlexaffvenue
Kurt W. Clausen

Bibliographic record

VenueThe Canadian Journal of Action Research · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsNipissing University
Fundersnot available
KeywordsAction researchOutcome (game theory)Action (physics)PsychologyEngineering ethicsMathematics educationEngineeringEconomics

Abstract

fetched live from OpenAlex

Traditional research methodologies are all about control.Above everything else, the researcher endeavours to isolate variables so that one research question can be asked and answered in no uncertain terms.Any spin-off effects are eschewed as unwanted by-products of poor design and execution.And, in the world of pure science, this is a very good mind-set indeed.Did it not lead to the discovery of many a miracle cure?A new element?An untried mechanism?However, in most cases, one unavoidable consequence of research (whether the researcher desires it or not) is collaboration.For all the myths of the lone scientist, hermit-like in a cell-shaped laboratory, it cannot be denied that most effective research takes the form of partnerships and group work: Banting had Best; Marie had Pierre; and the Manhattan Project needed an army of researchers to achieve results.In the end, one of the (albeit unintended) outcomes of most research undertakings is proof that collaboration works.This remains implicit in most write-ups and reports, but nevertheless, it must be recognized.When the "founder of social psychology," Kurt Lewin, set out the Action Research model in 1944 while at MIT, he distinguished it as a core aspect of his study of group dynamics.In his 1946 paper "Action Research and Minority Problems", he described it as "a comparative research on the conditions and effects of various forms of social action and research leading to social action" (p.35), accentuating not only the study of personal interactions, but including personal interactions and leading to personal interaction.Now, over time, action research has evolved into a myriad of approaches under one umbrella.But, I must agree with Margaret Riel, Director for the Center for Collaborative Action Research, who argued that while the method involves a deep inquiry into one's professional practice, "it is also a collaborative process as it is done WITH people in a social context, and understanding that change means probing multiple understanding of complex social systems" (Riel, 2020).On a more practical note, Riel added that the underlying implication was that all action researchers followed a commitment to data sharing.

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)0.248
metaresearch head score (Gemma)0.250
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.248
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2480.250
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.004
Science and technology studies0.0170.089
Scholarly communication0.0270.059
Open science0.0070.035
Research integrity0.0160.030
Insufficient payload (model declined to judge)0.0090.004

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 head0.898
GPT teacher head0.720
Teacher spread0.178 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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