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Record W2967770988 · doi:10.33524/cjar.v18i2.333

INSIDE THE BLACK BOX: TRACKING DECISION-MAKING IN AN ACTION RESEARCH STUDY

2018· article· en· W2967770988 on OpenAlexvenueno aff
Cathryn Smith

Bibliographic record

VenueThe Canadian Journal of Action Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFolioAction researchFacilitatorSession (web analytics)Black boxChecklistMathematics educationAction (physics)PsychologyPedagogyComputer scienceSocial psychologyArtificial intelligenceArt

Abstract

fetched live from OpenAlex

Action research has been described as “designing the plane while flying it” (Herr & Anderson, 2005, p. 69). A black box documented the researcher’s decisions while facilitating leadership development sessions with teacher leaders. Ten process folio steps informed the study through six iterations. Planning steps included a design frame, researcher’s action research cycle, session agenda, facilitator’s guide and researcher’s checklist. Observation steps included videotape transcriptions and written participant and researcher reflections. Analytical steps included a logic model, session summary and cumulative themes. Secondary analysis across the data sets crystalized the research contributions. The black box effectively documented complex 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)0.212
metaresearch head score (Gemma)0.261
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.212
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2120.261
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.008
Science and technology studies0.0110.012
Scholarly communication0.0170.021
Open science0.0040.013
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0110.006

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 head0.729
GPT teacher head0.627
Teacher spread0.103 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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