Bibliographic record
Abstract
This chapter explores an Australian Indigenous Wiradjuri standpoint. It expresses an emotional chronicle of dispossession creating an Aboriginal diaspora. Exclusion, loss of freedom, close surveillance and prohibitions, are stories framed within histories of emotion. Storylines in archives and oral histories of clan and tribal connection to Country speak of trauma, disconnection and forced removal to places far away. Policies of segregation establishing missions and reserves were sources of homesickness and grief. Experiences created narratives resonating within a history of emotions. Yet ongoing impacts on Aboriginal society and culture through this compelling rendering of ancestral history also speak of survival, resilience, and joy. Emotions in truth-telling contexts resonate our commonalities globally: Sami, Hawaiian and Canadian colonial experiences. From a dialectic perspective, Indigenous people are refugees in their own lands. Cultural and historical knowledge of Indigenous people through emotion and stories of feeling resonate as recordings of devastation, but also hope. Indigenous people’s story-telling describes social commentaries of interrelatedness in connection to country channelling healing and love. Attempted cultural genocide and land expulsion make way for transformative times of shared Indigenous voices, reflected globally in the amity of the United Nations Declaration on the Rights of Indigenous Peoples, seeking harmonious solutions.
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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 | 0.001 |
| 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 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".