The impact of engagement in front‐line service roles on the subjective wellbeing of Indigenous employees
Bibliographic record
Abstract
Indigenous (Aboriginal) populations in advanced economies such as Australia, Canada, New Zealand and the US are severely disadvantaged in comparison to wider society across most socioeconomic, health and wellbeing indicators (Manning, Ambrey and Fleming, 2016). For example, in Australia when compared to the community at large, Aboriginal members of the Australian society are still overrepresented in key social areas such as infant mortality rates, poor school attendance, literacy and numeracy levels, and labor force participation (Commonwealth of\nAustralia, 2017). Moreover, according to ‘Australia’s Health 2016’, a recent report by Australian Institute of Health and Welfare (2016), there are large gaps between Indigenous and non-Indigenous Australians on many health and well-being measures, after adjusting for differences in age structure. However, effective solutions to this ongoing policy concern is further complicated in Australia because of the long history of endemic racism towards the Indigenous community.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".