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Record W7133044562

The Customer Isn't Always Right: Exploring the Protective Ambit of the Employment Protection in the Ontario Human Rights Code through Customer-on-Worker Harassment

2016· dissertation· W7133044562 on OpenAlexfundaboutno aff
Gregory Tsang Tai Ko

Bibliographic record

VenueTSpace · 2016
Typedissertation
Language
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsSection (typography)DutyHarassmentCover (algebra)Statutory lawHuman rights
DOInot available

Abstract

fetched live from OpenAlex

This thesis argues that the "right to equal treatment with respect to employment" under section 5(1) of the Ontario Human Rights Code extends to protect workers from customer harassment. In so doing, this thesis aims to provide an account of section 5(1) and the way in which it imposes duties on parties who do not fit the traditional definition of an employer. This project argues that the statutory language of section 5(1) demonstrates a legislative intent to cover relationships outside of the paradigmatic employer-employee relationship and that it is broad enough to cover customer-worker interactions. This thesis will further argue that there is sufficient proximity between customers and employees to justify extending a duty onto customers to refrain from harassment. Finally, this project demonstrates that broader policy considerations justify extending section 5(1) coverage to address customer-worker harassment.

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)0.005
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.137
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.031
Scholarly communication0.0110.004
Open science0.0020.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.135
GPT teacher head0.413
Teacher spread0.278 · 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
Published2016
Admission routes2
Has abstractyes

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