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Record W2749142400 · doi:10.18432/r22s75

Engaging Pre-Service Teachers in the Drama in Teacher Leadership

2017· article· en· W2749142400 on OpenAlexaffvenue
Jerome Cranston, Kristin Kusanovich

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsScripting languagePedagogyClass (philosophy)Qualitative researchFocus groupEducational leadershipDramaPsychologyParticipant observationInjusticeSociologySocial psychologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper presents the findings of a qualitative research study that examined the effects of a transdisciplinary ethnotheatre workshop designed to support the professional development of school leaders as they navigate the complexities of teacher leadership. The site of inquiry was a pre-service teacher leadership workshop held in a graduate school class where participants analyzed, witnessed, and enacted ethnodramas, problematizing tensions in teacher leadership. Using a constant comparative approach, the participant journals were read and re-read, coded, and then categorized thematically with particular focus on negative case analysis. The authors present excerpts from the ethnodrama scripts used in the workshop alongside the research findings, which suggest that, while some participants perceived the injustice of a given situation presented in the ethnodrama, and could articulate how the process of ethnotheatre created avenues for learning about the lived reality of teacher leaders, others indicated either a lack of awareness or a tacit acceptance of the bullying and discriminatory behaviors embedded within the scripts.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.013
Scholarly communication0.0050.004
Open science0.0020.007
Research integrity0.0020.004
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 head0.513
GPT teacher head0.540
Teacher spread0.027 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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