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Record W3091664097 · doi:10.31355/53

Article 3 from Series of 5: Black-White Differences in Canadian Educational Attainments and Earnings

2019· article· en· W3091664097 on OpenAlexaboutno aff
Rosalie Masella, James McIntosh

Bibliographic record

VenueInternational Journal of Community Development and Management Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRespondentEarningsCensusEducational attainmentEducational inequalityBlack maleDemographic economicsPsychologyInequalityDemographyActuarial scienceEconomicsPolitical scienceSociologyAccountingEconomic growthPopulationMathematics

Abstract

fetched live from OpenAlex

NOTE: THIS ARTICLE WAS PUBLISHED WITH THE INFORMING SCIENCE INSTITUTE. Aim/Purpose.................................................................................................................................................................................................... Data from two large Canadian surveys are used to analyze educational and earnings performance of Blacks and Whites. The main purpose of this study is to determine how well blacks perform relative to Whites in these two areas. Background........................................................................................................................................................................................................ Canadian researchers have been studying black performance in education and labor markets since the 1970’s. Much of this research was done before 2000. It showed that there was considerable discrimination in the way Blacks were treated in the labor market but fewer problems concerning their success in the educational system. Since then more data has become available and it is possible to re-examine this issue and explore new dimensions of black economic performance. Methodology..................................................................................................................................................................................................... Educational outcomes are categorical and are analyzed by ordered Beta probability models. Earnings functions are estimated by mixed linear regression models where the mixing procedure is used to account for unobserved differences in respondent ability. Contribution....................................................................................................................................................................................................... Our results update and expand what was known before 2016 using the most resent Canadian Census and Youth Smoking Survey of which the latter contains academic performance information for students in primary and secondary school. Findings.............................................................................................................................................................................................................. The main results show that while Black males are able to access the educational system without much racial prejudice, they are not treated fairly in the labor market. Black females do less well in both the educational system and labor markets. Blacks earn significantly less than Whites for all age groups, all levels of education, and in all occupations. They are more likely to be less than fully employed and more likely to be at the bottom of the income distribution. These findings are consistent with earlier studies but the amount of discrimination is larger and black/white earnings differentials are larger than those found by researchers using earlier surveys. Recommendation for Practitioners .................................................................................................................................................................. These results are disturbing and the persistence over long periods of time suggests that some form of expanded government intervention is needed. Recommendations for Researchers................................................................................................................................................................. The surveys used here provide inadequate information on the process of discrimination. More and better data is needed to understand why, for example, black students do less well than their white counterparts in primary and secondary school and yet overcome these problems in tertiary education. Impact on Society.............................................................................................................................................................................................. Discrimination of any sort is costly to the victims but is also detrimental to society as a whole since it represents a failure our institutions to deliver a fair and just society for all groups regardless of race or ethnicity. We hope our results will draw attention to the need to address this problem.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0850.006

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 head0.031
GPT teacher head0.309
Teacher spread0.277 · 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 routes1
Has abstractyes

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