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Record W2978803726 · doi:10.82396/cjcd.v17i1.3126

The Effect of Social Variables on the Career Aspirations of Indigenous Adults in New Brunswick

2021· article· en· W2978803726 on OpenAlexaffabout
Michael Hennessey, Jeffrey Landine

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIndigenousMarital statusCompromisePsychologyCareer developmentSocial psychologyGerontologySociologyDemographyMedicineSocial science

Abstract

fetched live from OpenAlex

A recent survey of Indigenous peoples in New Brunswick by the Joint Economic Development Initiative Inc. (JEDI) collected data regarding social factors that influence career aspirations. These social variables included: gender, marital status, education level, social welfare dependency, and mobility. These variables were analyzed for their significant differences with career aspirations, measured by O*Net Job Zones. A final data set of 202 survey respondents was used for data analysis. The results aligned with Gottfredson’s theory of compromise and circumscription as preparation showed a significant effect with career aspirations. Factors outside of that framework, including marital status, gender, social dependence, and mobility, were also analyzed. The findings showed that women participants had greater career aspirations than men, and that less mobile participants had higher career interests. Implications for Indigenous career development theory, future research, and career counselling are discussed.

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)0.002
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 score0.548
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.227
Teacher spread0.215 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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