From Knowledge to Wisdom: Indigenous Women's Narratives of Doing Well with Career Decision Making
Bibliographic record
Abstract
Indigenous women in Canada are outperforming other Canadians in the labour market (DePratto, 2015). However, we currently have limited understanding about how Indigenous women decide on their choice of career. We sought to understand Indigenous women’s narratives of doing well in making career decisions. Ten women volunteered to tell their stories of how they made career decisions that resulted in positive outcomes. Using a narrative research design, in-depth interviews were recorded and narrative accounts were generated that illuminated the ways in which women in this study overcame life circumstances in their quest to establish a career. Verbatim transcriptions and individual narrative accounts were constructed. The narratives were then analyzed using a thematic analysis (Braun & Clarke, 2006). All participants confirmed the following five main themes: (1) focusing on a career direction, (2) pursuing further education and training, (3) overcoming and learning from adversity, (4) relational experiences that influenced career decisions and (5) connection to Aboriginal community as part of career decision-making. Implications for future research, career theory development and education as well as career counselling practice are discussed. part of career decision-making. Implications for future research, career theory development and education as well as career counselling practice are 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".