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Record W7057292372

Income inequality between visible minorities and whites: A comparison between Quebec and the rest of Canada

2018· dissertation· en· W7057292372 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRest (music)InequalityEconomic inequalitySocial inequalityEthnic groupIncome distribution
DOInot available

Abstract

fetched live from OpenAlex

Objectives. Studies that have examined wage differentials between visible minorities and whites living in Canada found that, even though most immigrants since the 1990s have had similar levels of educational attainment (even greater) relative to native-born Canadians, most have a lower income compared to the latter group, especially visible minority immigrants. Few of those studies, however, have examined income inequality experienced specifically by visible minorities, concentrating instead on immigrants overall. This constitutes an important gap in the literature given that visible minorities, both native-born and immigrants, are more likely to experience a wage gap with respect to native-born whites than white immigrants. In addition, only a small number of studies have taken a detailed look at income inequality experienced by visible minorities living in Quebec, compared to visible minorities living in the rest of Canada. This is an important limitation given that Quebec is one of the Canadian provinces where much of the conflict regarding the integration of individuals who might appear as different from the majority is occurring. Thus, this dissertation aims at addressing those lacunas by studying wage differentials between visible minority members and whites, comparing Quebec to the other Canadian provinces.Method. This dissertation uses the 2006 Census and 2011 National Household Survey and employ the Oaxaca-Blinder decomposition model in order to separate the wage gap into two parts: a part that can be explained by sociodemographic and human capital characteristics, and a part that cannot be explained by such observable characteristics and, thus, more likely due to discriminatory practices. Results. The first analysis demonstrates that there are significant wage differences between visible minority groups and white individuals in Quebec, but that these wage gaps are mostly explained by individual characteristics. However, unexplained portions of these wage gaps remain, potentially indicating the presence of discrimination. Moreover, the rest of the analyses show strong evidence that some minority groups who live in Quebec might be more financially disadvantaged than visible minorities living in the rest of Canada. However, the reason why Quebec has the largest wage gaps seem in large part because of the disadvantageous socio-demographic and human capital characteristics that visible minorities living in Quebec have. Conclusion. Findings reveal that most of these large gaps are explained by the fact that visible minorities who live in Quebec have characteristics that disadvantage them in the labor market. This might be related to Quebec’s immigration policies that differ slightly from the rest of Canada. Thus, social policies should concentrate even more on language training courses as well as other integration initiatives. However, findings also reveal the presence of discrimination in the province of Quebec when it comes to wage differentials, especially when it comes to the language and the nativity and duration status of visible minorities, which should be acknowledged as well.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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