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Record W4392815961 · doi:10.29173/jaed349

Examining Aboriginal Employment: 2007–2014

2015· article· en· W4392815961 on OpenAlexaffabout
Robert Oppenheimer

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsConcordia University
Fundersnot available
KeywordsUnemploymentMetisUnemployment rateDemographyGeographyDemographic economicsEconomicsSociologyEconomic growth

Abstract

fetched live from OpenAlex

The employment, unemployment and participation rates are examined for Aboriginals living off-reserve in Canada from 2007 to 2014 as well as for non-Aboriginals for those 15 years and older. Employment is analyzed by educational level, gender and age, province and by industrial sector. The rates of employment and unemployment for Aboriginals have continued to improve in 2014, lessening the differences with non-Aboriginals. These improvements occurred in each age category for both men and women, except for the unemployment rate of Aboriginal women in the 25 to 54 year old group. The Metis employment, participation and unemployment rates have been more favourable than the First Nations for each year from 2007 to 2014. The level of education obtained continued to show a strong positive relationship with the rate of employment for Aboriginals and non-Aboriginals. When employment rates are examined by educational level, there is very little difference between Aboriginals and non-Aboriginals. However educational level does not explain the differences in unemployment rates. The highest rate of employment for Aboriginals and non-Aboriginals in 2014 continued to be in Alberta. Employment continued to increase in the services-producing sector, but decreased in the goods-producing sector for Aboriginals and non-Aboriginals in 2014.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.334
Teacher spread0.303 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2015
Admission routes2
Has abstractyes

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