Cross-cultural Organizations and the Empowerment of First Nations Learners
Bibliographic record
Abstract
By examining the tensions around First Nations learners wedged between competing organizational visions, this research exposes the conflicting funding enticements that impede maximized empowerment for First Nations adult learners. In a mixed methods ethnographical case study using a social justice theoretical framework, this study documented promising levels of empowerment for the students at the beginning of the program. These levels of empowerment were eroded, however, by the Eurocentric funding model that pitted the expectations of First Nations organizations against those of the institutions offering the program, and the needs of the students themselves. The data indicated that the mandatory workplace courses delivered to the informants later in the study were generally below the informants? ability range. Ensuing levels of empowerment of the learners near the end of the study appeared to reflect the economic streamlining decisions with data that indicated disempowerment across several quantitative categories as run through SPSS and supported by the study?s side-by-side qualitative data. Endorsements for Ministry of Education and Ministry of Training, Colleges and Universities to focus their funding models on quality, rather than the quantity, of programming are among the recommendations that emerge from the research. Recommendations also include utilizing graduate level teachers working with management rather than under management to facilitate assured and embedded front line input into program development.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".