Job Satisfaction And Aboriginal Labour Mobility Among Nonreserve Populations: An Overlooked Variable?
Bibliographic record
Abstract
With a focus on the non-reserve Aboriginal population of Manitoba, this article examines the extent to which job satisfaction influences labour mobility. The study incorporates into the analysis a comparison of this factor to such other variables as job security, gender, age, education, as well as Aboriginal identity (i.e., First Nations, Métis, etc.). The author concludes that among those who are employed in the non-reserve population, two variables stand out as being statistically significant in association to mobility: age and job satisfaction. At the same time, many other variables in this population of employed individuals do not stand out as being statistically significance, most notably gender and education. It is hoped that this paper will further discussions among policy makers and program strategists regarding the "whys" behind population mobility rather than simply the "who" when addressing issues involving labour mobility. This study therefore pertains directly to the topics of demography, human resources, and social policy as they together apply to Métis, non-status Indians, and First Nations people residing off-reserve. The results for this study are based on a random and representative sample of 1,019 self-identified Aboriginal respondents, of which 362 were employed full-time at the time of the interview and were residing in a non-reserve community in Manitoba.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".