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Record W2604237238

Generalized harassment in Canadian universities: policies and practices addressing bullying in the academic workplace

2010· dissertation· en· W2604237238 on OpenAlexfundaboutno aff
Justine Brisebois

Bibliographic record

VenueMspace (University of Manitoba) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsHarassmentWorkplace bullyingPolitical sciencePublic relationsPsychologyPedagogySocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the implications of anti-harassment policies at Canadian medical-doctoral universities. The problem of generalized harassment as a phenomenon of academic bullying is identified and defined. This thesis explores how anti-harassment policies and practices of Canadian medical-doctoral universities have come to be, as well as their implications for academics. Chapter one identifies the methodology of the thesis, a comparative policy analysis of the policies and practices of Canada's medical-doctoral universities. Chapter two describes the theoretical foundations used in the thesis: theories of academic organizational control, policy formation, problem representation, and manifest and latent functions. Chapter three reviews contemporary literature on the role of universities in society and the phenomenon of generalized harassment in academia. Chapter four reports the results of a comparative analysis of the anti-harassment policies and practices of Canada's medical-doctoral universities, which reveal three approaches to anti-harassment policy. Chapter five links the theoretical to the empirical in order to better understand the phenomenon of generalized harassment in Canadian medical-doctoral universities, and the implications policies and practices have for the future of collegiality.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.543
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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