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Record W7162108841 · doi:10.82308/4211

Learning to discipline students in Montreal schools: An administrator's perspective

2019· dissertation· en· W7162108841 on OpenAlexaboutno aff
Nathalie Cheff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)DisciplineEthnographyJudgementCoachingRelation (database)Work (physics)Action (physics)

Abstract

fetched live from OpenAlex

Using the lens of educational institutional ethnography, this dissertation explores how the disciplining of students is textually and socially organized in Montreal (Quebec) schools. This qualitative study investigates, first how school administrators learn to discipline students. Secondly, this research project considers how different stakeholders in schools (e.g., teachers, administrators, students and parents) view school discipline differently. Concentrating on the school administrators' description of the work they do on a daily basis in elementary and high schools, I used ethnographic data (interviews, textual analysis and journaling) to discover how discipline is organized and how administrators learn to do it and think about it in the ways that they do. Findings suggest that new vice-principals learn to do their work in action with the people they work with and through coaching from their principal, rather than simply basing their decision-making process on common sense and professional judgement as they initially suggested. The study also found that teachers play an important role in how students are disciplined in a school and have a tendency to favor exclusionary disciplinary measures when other measures have failed. Both of these findings bring about concerns for biases and discrimination against students with special needs and from visible minorities who could be targeted by these exclusionary disciplinary measures. Finally, the research project illuminated a disconnect between teachers and administrators' expectations in relation to disciplining students.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.468
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.465
Teacher spread0.430 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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