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Record W2952879402 · doi:10.5206/uwojls.v9i2.8069

Assessing the Damage: Money Awards by the OHRT in Sexual Harassment Cases

2019· article· en· W2952879402 on OpenAlexvenueaboutno aff
Honor M. Lay

Bibliographic record

VenueWestern Journal of Legal Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentDamagesEntitlement (fair division)PsychologyTribunalCriminologyPolitical scienceSocial psychologyLawEconomics

Abstract

fetched live from OpenAlex

Individuals who experience sexual harassment in employment, housing, education, or other social services in Ontario may be entitled to a general damages remedy under section 45.2 of the Ontario Human Rights Code. The Ontario Human Rights Tribunal conducts an objective analysis of the severity of the harasser’s conduct and a subjective analysis of the impact of the incident on the applicant. Generally, the more severe the conduct or impact on the applicant, the higher the award for general damages. Due to an expectation that individuals will appear traumatized after enduring sexual harassment, an applicant’s failure to produce evidence of distress or traumatization will often adversely affect his or her entitlement to higher damage awards. This paper argues that the requirement to produce medical proof is an unwarranted invasion of the individual’s right to medical privacy, unjustly imposes an additional evidentiary burden upon the applicant, and perpetuates the myth surrounding sexual assault that trauma is visible and uniform.

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)0.008
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.004
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.107
GPT teacher head0.445
Teacher spread0.338 · 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 designObservational
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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