Cultural Infusions and Shifting Sands
Bibliographic record
Abstract
Indigenous young adults experience disproportionately high rates of unemployment, which are exacerbated by systemic factors such as poverty and oppression (Britten & Borgen, 2010). Despite these challenges, many Indigenous young adults do well in their educational and employment pursuits (Bougie et al., 2013). This study explored what helped and hindered the career decision-making of 18 Indigenous young adults in Canada who see themselves as doing well in this regard. Using the Enhanced Critical Incident Technique (ECIT), a qualitative research method which focuses on helping and hindering factors (Butterfield et al., 2009), 13 categories were identified: (a) Family/Relationships & commitments, (b) Setting goals/Taking initiative/Focusing on interests, (c) Support from community/mentors, (d) A healthy way (physical, mental, social), (e) Finding meaning/motivation & contributing, (f) Networking & who you know, (g) Systemic/External factors (institution, job-market, sexism, racism, interpersonal aspects), (h) Financial situation, (i) Knowledge/Information/Certainty, (j) Experience (work/life), (k) Educational opportunities/Training & specialized education, (l) Indigenous background/Cultural factors, and (m) Courage & self-worth (vs. fear/doubt in self/others). These categories highlighted the systemic, interpersonal, and experiential processes in career decision-making for Indigenous young people in Canada. Implications for career counselling practice and future research are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".