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Record W2901281073 · doi:10.17722/ijme.v11i3.1036

Principal Procurement Policy Analysis in Achieving School Management Excellence

2018· article· en· W2901281073 on OpenAlexvenueno aff
Dikdik Supriyadi

Bibliographic record

VenueInternational Journal of Management Excellence · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPrincipal (computer security)DocumentationProcurementExcellenceData presentationData collectionPublic relationsMathematics educationPolitical scienceBusinessSociologyComputer sciencePsychologyMarketing

Abstract

fetched live from OpenAlex

This research of principal procurement policy focuses on organizing Principal Leadership Preparation Program (in Bahasa Indonesia is abbreviated as PPCKS) activities with participants coming from public elementary school teachers and state junior high school teachers. The research method is qualitative using case study approach with purposeful sampling. The implementation strategy of data collection through validity test, followed by triangulation of data in the form of interview, documentation study and observation. Data analysis used is data reduction, data presentation and data verification. The results of this study illustrate that: 1) the issue of education policy in the school principal procurement sometimes raises the dualism of interests normatively and politically; 2) the implementation of school principal procurement policy through PPCKS shown that it seems the committee from Educational Office element only role in the recruitment only, beyond that all controlled by LPPKS and Educational Office only did the monitoring; 3) most principals do not have PPCKS follow-up programs. Each principal should have a program and strategy in preparing candidates for the principal to be able to produce an effective principal.

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 score0.493
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.355
Teacher spread0.324 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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