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Record W7162028664 · doi:10.82308/20033

Secondary school administrative teams : issues and processes

2001· dissertation· en· W7162028664 on OpenAlexaboutno aff
Lauren E. Small

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringVariety (cybernetics)Work (physics)Data collectionQualitative researchFunction (biology)Descriptive research

Abstract

fetched live from OpenAlex

Educational institutions in Quebec face a wide variety of challenges as major restructuring efforts take place. Schools must develop strategies to manage these increasing demands in an effective manner. The purpose of the study was to contribute to the understanding of school administrative teams and to shed some light on the nature and function of these teams through a study of their administrative team meetings. As a qualitative descriptive study, this research involved 24 secondary school administrators. Data collection techniques included postal surveys to gather initial descriptive data, followed by telephone interviews that allowed for more in-depth discussion of issues faced by the principals and their administrative team. The findings provide strong evidence of the complexity of the nature of secondary school principals, work and the importance of working collaboratively with their administrative colleagues. The study has implications for the preparation of aspiring educational administrators, as well as those who currently hold these roles in today's 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)0.016
metaresearch head score (Gemma)0.025
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 score0.446
Threshold uncertainty score0.886

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0330.015
Scholarly communication0.0200.004
Open science0.0030.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.108
GPT teacher head0.453
Teacher spread0.345 · 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
Published2001
Admission routes1
Has abstractyes

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