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Record W2801344568 · doi:10.22230/ijepl.2018v13n2a730

Educational Decentralization Efforts in a Centralized Country: Saudi Tatweer Principal Perceptions of New Authorities Granted

2018· article· en· W2801344568 on OpenAlexvenueno aff
Salah S. Meemar, Sue Poppink, Louann Bierlein Palmer

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationPrincipal (computer security)PerceptionPublic relationsBusinessPolitical sciencePublic administrationPsychologyComputer science

Abstract

fetched live from OpenAlex

This study captured the perspectives of school principals in Saudi Arabia regarding the new authorizes granted to them as part of their country’s education decentralization efforts. Specifically, this study explored these principals’ perceived ability to implement the new authorities, levels of support, effectiveness, and additional desired authorities. This study provided an opportunity to analyze the early efforts of a country with a very centralized educational system to implement more significant decentralization efforts.A total of 173 Tatweer school principals completed an online survey, and findings suggest these Saudi principals perceived limited ability, low to moderate support in implementing the new authorities, and only slight agreement that the authorities were likely to achieve desired outcomes. Multiple regression analysis revealed that beliefs on the effectiveness of the authorities at achieving MOE outcomes were predicted by perceived ability to implement administrative authorities, perceived support to implement technical authorities, and years of experience.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.398
Teacher spread0.319 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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