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Record W4405138550 · doi:10.53555/sfs.v10i1.3213

A Study of the Relationship between Leadership, School Culture and Achievement of Students at the Secondary Level in Kailali, Nepal

2023· article· en· W4405138550 on OpenAlexvenueno aff
Chandrakala Dawadi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics education

Abstract

fetched live from OpenAlex

This study explores the relationship between leadership styles, school culture, and the academic achievement of secondary-level students in Kailali, Nepal. The primary objectives are to analyze the impact of different leadership styles on student achievement, evaluate the role of school culture in shaping educational outcomes, and investigate the interplay between leadership and school culture in influencing student performance. A qualitative method research was adopted to ensure a comprehensive analysis. Data were collected from a sample of secondary schools in Kailali using surveys, structured interviews, and academic performance records. Data from interviews were thematically analyzed to capture nuanced insights. The findings indicate that transformational leadership styles positively influence student achievement by fostering a collaborative and motivating school environment. Additionally, a strong, positive school culture characterized by shared values, high expectations, and effective communication enhances academic outcomes. The study also highlights the synergistic effect of leadership and school culture, emphasizing that the two factors together significantly improve student performance compared to their independent effects. The research underscores the importance of capacity-building programs for school leaders and initiatives to strengthen school culture. These findings provide actionable insights for policymakers and educators aiming to improve secondary education outcomes in similar contexts.

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)0.006
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 score0.021
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.645
GPT teacher head0.427
Teacher spread0.218 · 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
Published2023
Admission routes1
Has abstractyes

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