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Record W6992282750

Leadership styles, school effective, needs of 'mien' (face) behaviour: the interactions in Hong Kong private schools

2001· dissertation· en· W6992282750 on OpenAlexfundno aff

Bibliographic record

VenueDurham e-Theses (Durham University) · 2001
Typedissertation
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
FundersEgg Farmers of Canada
KeywordsNature versus nurtureRighteousnessInterpersonal communicationLeadership styleContext (archaeology)Interpersonal relationshipStyle (visual arts)
DOInot available

Abstract

fetched live from OpenAlex

National cultures vary and the variations challenge the conventional wisdom of the Western management theory and practice in other cultural contexts. Specifically, the national characteristic of 'face(mien) behaviour' is immensely important at all levels in Chinese interpersonal communications. The notion of 'mien' permeates every aspect of interpersonal relationships in Chinese culture because of the culture’s overarching concern with relationships. This study examines the nature of 'mien' behaviour, explores how 'mien' functions in the Hong Kong educational context, and how leadership styles of secondary school principals interact with 'mien' as perceived by their teaching staff and how, eventually, these interactions influence the effectiveness of the schools. Whenever Chinese behaviour is discussed, the social philosophy of Confucianism is relevant. The Confucian ethical system regulating social behaviour has three principle ideas: ren(), yi() and li(); benevolence, righteousness or justice, and propriety or courtesy. This study also examines how these three principles nurture 'mien' and considers whether any alternate style of leadership in Hong Kong context can be formulated.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
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 head0.082
GPT teacher head0.328
Teacher spread0.246 · 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 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
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueDurham e-Theses (Durham University)Same topicCultural Differences and ValuesFrench-language works237,207