Bibliographic record
Abstract
Long before the official Apology by Canadian Prime Minister Stephen Harper to the victims of the residential schools system, the previous Canadian Government had handed over $350 million compensation for those Aboriginal people who suffered abuse as wards of the state. This was wisely invested into culturally significant initiatives such as talking circles, language revitalisation, digital storytelling and other projects. Such arts initiatives are not only for the good of those individuals that endured horrific treatment under the government, church and the dominant mainstream population, but their descendants, families and tribes, along with the wider community. This is a suitable model for Australia to follow in attempting to address the impact of the horrendous wrongs of the past on an individual and collective level, perhaps allowing us all to move forward with a healthier mindset. However, we wait for that type of meaningful ‘money where your mouth is’ gesture of commitment from the Australian Government in retrospect. Meanwhile at the community level, some artists from around Australia are proactively making headway into maintaining what I like to call the ‘Healing Arts’, highlighting the processes and issues regarding the wellbeing of Aboriginal people and also the inter-relatedness of others. The Healing Arts have the expressive potential for us as a multitude of Aboriginal cultures to enable and effect change for ourselves, while also participating in the mainstream social constructs known as ‘health’, ‘culture’ and ‘the arts’. Some of those leading the way in both ancient and newer forms of Aboriginal expression are Gulumbu Yunupingu, Emma Donovan and myself.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".