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Record W4234753337 · doi:10.24124/2012/bpgub806

Human Rights and the Duty to Accommodate in Employment: Stakeholders' Knowledge and Attitudes.

2012· dissertation· en· W4234753337 on OpenAlexaboutno aff
Daniel Huang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleDutyPsychologyScale (ratio)Educational attainmentSocial psychologyPolitical scienceDevelopmental psychologyGeographyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The purpose of this study was to measure the level of knowledge and the type of attitudes of key stakeholders on human rights and the duty to accommodate in employment in Canada. Two survey instruments, a 20 item true or false knowledge questionnaire and a 20 item seven point Likert scale attitudes questionnaire, developed by the researcher based on literature review, were administered to 160 participants. Both quantitative and qualitative analyses were utilized for this study. Among all participants, the results indicated a general low level of knowledge with a mean score of 11.20 (SD = 2.317) and slightly positive attitudes with a mean score of 90.17 (SD = 14.098) on human rights and the duty to accommodate. Specifically, analysis indicated participants in the human resource occupation, in higher income brackets, working in larger organizations, in a unionized environment and in the public sector have more knowledge than their counterparts. There was also statistical significance for participants in the higher income brackets and working in a unionized environment demographic on the attitudes questionnaire. With respect to correlation factors, both academic attainment/knowledge level and knowledge/attitudes were slightly positively correlated at a statistically significant level. However, in both cases the coefficient of determination (R²) was relatively low at 0.021 and 0.068 respectively. Therefore, the variability of knowledge based on academic attainment and attitudes based on knowledge share 2.1% and 6.8% respectively. In essence, 97.9% and 93.2% of variability can be accounted for by other variables. --P. ii.

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 score0.495
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.082
GPT teacher head0.378
Teacher spread0.296 · 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
Published2012
Admission routes1
Has abstractyes

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