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Record W7001752424

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

2019· dissertation· en· W7001752424 on OpenAlexaffabout

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsMcGill University
Fundersnot available
KeywordsEthnographyDisciplinePerspective (graphical)JudgementCoachingQualitative researchRelation (database)Action (physics)
DOInot available

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. II RSUMEn s'inspirant de la thorie de l'ethnographie institutionnelle en ducation, cette thse explore comment la discipline des lves est organise textuellement et socialement dans des coles montralaises (au Qubec).Cette tude qualitative enqute premirement sur la faon dont les directeurs d'cole apprennent discipliner les lves.Deuximement, ce projet de recherche tient compte de la faon que les nombreux partenaires de l'cole (enseignants, directeurs, lves et parents) considrent diffremment la discipline des lves.En me concentrant principalement sur la description du travail fait au quotidien par les directeurs d'coles primaires et secondaires, j'ai utilis des donnes ethnographiques (entrevues, analyse textuelle et criture d'un journal professionnel) pour dcouvrir comment la discipline tait organise, apprise et perue par les diffrents partenaires.Les rsultats suggrent que les nouveaux directeurs-adjoints apprennent leur travail, incluant la discipline des lves, en travaillant au quotidien avec leurs collgues et travers le mentorat des directeurs avec lesquels ils travaillent troitement, plutt que d'utiliser le bon sens et leur jugement professionnel tel que suggr au dpart.L'tude a aussi dmontr que les enseignants jouent un rle trs important dans la discipline des lves dans une cole et vont en arriver prfrer les moyens disciplinaires d'exclusion lorsque d'autres moyens de discipline progressive ont chou.Ces deux rsultats amnent des proccupations par rapport aux jugements biaiss et la discrimination envers les lves besoins particuliers et ceux de minorits visibles qui pourraient tre viss par ces mesures disciplinaires d'exclusion.Finalement, ce projet de recherche dmontre une discorde quant aux attentes des enseignants et celles des directeurs au sujet de la discipline des lves.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.032
GPT teacher head0.384
Teacher spread0.353 · 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.

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 routes2
Has abstractyes

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