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Record W3048864786 · doi:10.1002/sres.2732

Leadership within action research: Surfacing the collective nature of leadership

2020· article· en· W3048864786 on OpenAlex

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

VenueSystems Research and Behavioral Science · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsSituatedConversationShared leadershipAction (physics)Leadership studiesPublic relationsPsychologySociologyLeadership styleManagementPolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract This article shares specific leadership findings from the evaluative study of action research AR (ESAR). Few studies have examined the leadership dynamic within action research (AR), and fewer still across multiple projects. Leadership surfaced as a critical element in the ESAR as we sought to examine processes, outcomes and impacts of AR at a meta level. Evidence was collected from six AR case study projects via interviews, survey, goal attainment scaling and documentary analysis, followed by a survey issued to 195 projects internationally (174 responded), that is, all of the projects recruited and compiled in our ‘Directory’ at the beginning of the ESAR. The findings revealed that leadership was more collaborative than hierarchical though there was evidence that a single key person leading was pivotal to enhancing processes, outcomes and impacts of the AR projects. In this article, we offer new thinking about leadership elements that enhance AR as well as contribute to the growing conversation about shared, collective and relational approaches to leadership situated within AR systems.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.054
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.012
Science and technology studies0.0030.004
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.920
GPT teacher head0.603
Teacher spread0.317 · 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