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Record W4307961833 · doi:10.5430/jct.v11n8p122

The Depiction of Servant Teacher Leadership Attributes in Mass Media: A Characterization Analysis of the Protagonist in Rita TV Series

2022· article· en· W4307961833 on OpenAlexvenueno aff
Olusiji Adebola Lasekan

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsDepictionServant leadershipPsychologyCharacter (mathematics)DramaSocializationPedagogyServantSocial psychologyLeadership styleLiteratureArt

Abstract

fetched live from OpenAlex

Taking into account the critical role of teacher leadership in school development and the importance of fostering its training through image media, this study aims to identify the ten proposed attributes of Greenleaf servant leadership exemplified by the protagonist of a popular comedy-drama television show called Rita. Adopting the notion of characterization, a qualitative study approach was used to identify the proposed Greenleaf's ten traits of servant leader (SL) demonstrated by the main character by evaluating her interactions with students and their parents, co-teachers, the principal, and school policymakers. The findings revealed that the character is an effective servant teacher leader, as her personality portrayed all the ten qualities proposed in Greenleaf SL. This implies that preservice and in-service educators can utilize the show as a reflective tool for enhancing Servant Teacher Leadership (STL) competencies in the classroom. This study contributes to the growing body of knowledge about teacher representation on television shows and the implications for teacher leadership education. A future research direction is also presented.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.352
Teacher spread0.300 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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