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Record W4285043982 · doi:10.22215/etd/2022-14972

Safe Schools for Whom? An analysis of policy, austerity and ‘workplace violence’ in Ontario elementary schools

2022· dissertation· en· W4285043982 on OpenAlexaboutno aff
Darby Mallory

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
FundersInstitute of Education Sciences
KeywordsAusterityChristian ministryLegislationHarmGovernment (linguistics)CriminologyPolitical scienceWork (physics)Workplace violenceSchool violenceSociologyPublic administrationPublic relationsPoison controlSuicide preventionPsychologyLawSocial psychologyPoliticsEngineeringMedicine

Abstract

fetched live from OpenAlex

Education workers, unions, and federations have been raising concerns about workplace violence in schools since the early 2000's. A document analysis of Ontario's elementary school legislation (2000-2020) documents how the Ministry of Education has accounted for student-on-teacher harm. While the Government of Ontario and the Ministry of Education have not yet acknowledged workplace violence in schools, the Ministry of Labour acknowledged issues of workplace violence in schools in 2018. This work demonstrates debates over defining and measuring rates of violence in Ontario schools and reflects on how academic attention on the issue has been divergent. My research highlights the prevalence and impacts of workplace violence, emphasizes the intersectional and gendered nature of the issue, and considers the concerns of conceptualizing elementary student behaviour as 'violent.' An anti-carceral approach to addressing workplace violence is discussed and recommendations for future research are presented.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0140.006
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.353
Teacher spread0.331 · 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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