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Record W4376611028 · doi:10.5430/jct.v12n3p108

Evaluation of Implementation Principal Leadership Management During the Covid-19 Pandemic

2023· article· en· W4376611028 on OpenAlexvenueno aff
Ucup Supriatna, Indra Kertati, Hendri Putra, Murtadlo Murtadlo, Sutrisno Sutrisno, Zunan Setiawan, Mardhiah Mardhiah, Nanda Saputra

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPrincipal (computer security)PandemicCoronavirus disease 2019 (COVID-19)Work (physics)Learning ManagementKnowledge managementComputer sciencePsychologyEngineeringMathematics educationMedicineComputer security

Abstract

fetched live from OpenAlex

This research is aimed to investigate and evaluate the implementation of principal leadership management during the pandemic of Covid-19. The Covid-19 outbreak in various countries has changed the traditional face-to-face learning pattern to online learning. Changes in face-to-face learning patterns to online learning must be followed by changes in work patterns for the principal as a manager. The question is, has the principal implemented leadership management following the standards required to carry out online learning during the COVID-19 pandemic? This evaluation study uses a discrepancy model (gaps), which determines the differences in the implementation of principal leadership management with the standards in the work guidelines of principals during the COVID-19 pandemic. The evaluation results concluded that the principal's leadership management implementation was not following the standards, that had implications for the quality of online learning implementation and student learning outcomes. Further research regarding the effectiveness of principal leadership management still needs to be done to improve school management in online learning.

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)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
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 integrity0.0000.000
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 head0.239
GPT teacher head0.442
Teacher spread0.203 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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