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Record W2125801534 · doi:10.5539/ass.v10n18p232

Challenges of Educators in the Context of Education Reform and Unrest: A Study of Southern Border Provinces in Thailand

2014· article· en· W2125801534 on OpenAlexvenueno aff
Kanita Nitjarunkul, Ekkarin Sungtong, Peggy Placier

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersIndiana University-Purdue University IndianapolisPrince of Songkla UniversityPurdue University
KeywordsUnrestCurriculumQualitative researchCoping (psychology)Context (archaeology)FeelingPolitical sciencePublic relationsPsychologySociologyMedical educationPedagogyGeographySocial scienceMedicineSocial psychologyPolitics

Abstract

fetched live from OpenAlex

This qualitative study aimed to identify challenges of educators faced with education reform and violent unrest that has taken place in five Southern provinces of Thailand, as well as leadership characteristics that emerged in this context. Participants were 21 educators from primary schools in Pattani, Yala, Narathiwat, Songkhla and Satun provinces. A purposeful selection was employed for participant recruitment of the study. Data collection methods were semi-structured interviews and related official document analysis. The study revealed that challenges of educators related to education reform were managing curriculum, increasing students’ reading competency, coping with work overloads, and managing limited budgets. Challenges related to social unrest were dealing with instructional management, coping with feelings, and ensuring safety. Leadership characteristics that emerged in response to these challenges were becoming patient, dedicated, and adaptive; guiding changes in instructional methods; and building collaborations with related stakeholders.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.395
Teacher spread0.326 · 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 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

Citations7
Published2014
Admission routes1
Has abstractyes

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