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Record W7133071533

High-school Principals’ Experiences of Building Positive Relationships with Students in Beijing, China

2022· dissertation· W7133071533 on OpenAlexaff
Yuan Chai

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsChinaQualitative researchConceptual frameworkQualitative analysisPhenomenonSemi-structured interview
DOInot available

Abstract

fetched live from OpenAlex

This qualitative study explores school principals’ experiences of building principal-student relationships (PSRs) in high schools in Beijing, China. Four aspects of relationship-building, namely, the meanings of PSR, the strategies of PSR construction, the facilitators, and the barriers of PSR-building are investigated. Many researchers have demonstrated that effective schooling is attributed to strong and positive relationships initiated by school administrators with their surroundings. In China, the national reform of education has given principals pressing missions of initiating and maintaining closer relationships with students. However, the phenomenon of PSR in Chinese schools remains unexplored. Accordingly, this research collected interview data from five Chinese high school principals who earned recognitions from principals in other schools, media news reports, and/or local educational authorities for their achievements in building and sustaining positive PSRs and identified meaningful themes through thematical analysis. Results of the analysis were organized into a conceptual framework of PSR-building in Chinese schools.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.005
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.018
GPT teacher head0.377
Teacher spread0.358 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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