Resisting the Far-Right: Indigenous Perspectives, Community Arts and Story-Based Strategy
Bibliographic record
Abstract
This article explores how we might resist and confront anti-immigration and anti-refugee politics by addressing the social and historical well-spring from which these discriminatory and damaging politics emerge and take sustenance. In doing this, I draw upon the concept of story-based strategy and the idea that our potential to address this issue relies on our capacity to fundamentally shift the dominant ways in which people understand and engage with it. This discussion occurs with reference to one practical application of story-based strategy – a community-arts project titled Stories of Hope and Migration – which attempted to re-frame the migration and refugee debate in Australia by funnelling it through a localised Indigenous perspective. In so doing, this article challenges the way in which early British migrants and their descendants have continually excised themselves from the rhetoric of migration, and furthermore, suggests that through a more nuanced conversation regarding the migration stories of all non-Aboriginal people, we might better promote a more historically aware, compassionate and inclusive society.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.059 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".