Yatdjuligin: the stories of Queensland Aboriginal registered nurses 1950–2005
Bibliographic record
Abstract
This research and its presentation as a dissertation was undertaken utilising an Aboriginal methodology. This methodology, known as Djarpligin, is used by South/Western Gurreng Gurreng people for the transference of our knowledges. Djaparligin translates to singing corroboree. This means it is bound to Gurreng Gurreng language, inclusive of our songlines and ceremonies. This methodology is utilised to tell the stories of the participants of this research. The findings of this research are the stories of the participants as they describe their journeys as Aboriginal women who are registered nurses. The presenting of this thesis is Yatdjuligin. An in-depth literature review was required for this research and it is more than a literature review but it is a component of my Djaparligin methodology. A thorough investigation was undertaken to look at the stories of Aboriginal nurses in Queensland and Australia. In constructing the stories of the research participants it was essential to contextualise them. First and foremost is their Aboriginality. This is entwined with the nursing history of each era and finally the government policies of the day. This was further contextualised by undertaking a substantial review of the literature to explore the voices of Indigenous nurses in the United States, Canada and New Zealand. The findings of this research are ‘Yatdjuligin: The Stories of Queensland Aboriginal Registered Nurses 1950–2005’.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".