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Record W4392815905 · doi:10.29173/jaed369

Attracting Aboriginal Youth to The Study of Business: Mentorship, Networking, and Technology

2016· article· en· W4392815905 on OpenAlexaboutno aff
Janice Esther Tulk, Mary Beth Doucette, Allan MacKenzie

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2016
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipCorporate governanceWork (physics)Public relationsPolitical scienceManagementSociologyMedical educationEngineeringMedicine

Abstract

fetched live from OpenAlex

There is widespread recognition that Aboriginal Canada needs more community members with business training to work in economic development and management, particularly with the growing development of natural resources in Aboriginal territories and self-governance initiatives. Yet, only 12% of funded Aboriginal students pursue post-secondary education in business or commerce. Barriers to the pursuit of tertiary education include inadequate student preparation and career guidance, lack of funding, and attitudes surrounding the ability to do math. The Purdy Crawford Chair in Aboriginal Business Studies at Cape Breton University addresses these barriers via its program for Aboriginal youth, which combines mentorship, networking, and technology to facilitate the transition from high school to post-secondary studies and engage students in business education. This article outlines the model employed by the Purdy Crawford Chair and assesses the initiative in relation to relevant literature on mentorship and technology.

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)0.007
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.318
Teacher spread0.291 · 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
Published2016
Admission routes1
Has abstractyes

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