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Record W4392824787 · doi:10.29173/jaed382

Conversations about Aboriginal Work Experiences: Reflections for Community Members, Organizations, and the Academy

2017· article· en· W4392824787 on OpenAlexaff
Wendi L. Adair, Catherine T. Kwantes, Twiladawn Stonefish, Ruxandra Badea, Warren Weir

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsVancouver Island UniversityUniversity of Waterloo
Fundersnot available
KeywordsMentorshipConversationIndigenousInterpretation (philosophy)Work (physics)SociologyPsychological resiliencePublic relationsEngineering ethicsPsychologyPedagogySocial psychologyPolitical scienceMedical educationMedicineEngineering

Abstract

fetched live from OpenAlex

The authors relate how they reflected upon, understood, and shared conversations about Aboriginal experiences at work across time and with different audiences. They found nuances in their understanding and interpretation as their audience changed from sharing circle members, to Cando conference attendees, and finally the Academy. Whereas initial impressions highlighted concepts of strength and resilience, which the authors translated into practical recommendations for mentorship and cultural safety, the results from an academic analysis highlighted how conversational focus changed when participants discussed work experiences in the past (systemic barriers emphasized), present (Indigenous worldviews emphasized), and future (all concepts discussed equally). The authors offer suggestions for continuing the conversation and new ways of understanding Indigenous employees' experiences at work.

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)0.033
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.048
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0750.034
Scholarly communication0.0160.010
Open science0.0050.024
Research integrity0.0110.022
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.377
Teacher spread0.348 · 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 designQualitative
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

Citations5
Published2017
Admission routes1
Has abstractyes

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