Digital technology and public apology: Responses by Indigenous Australians to a government saying sorry
Bibliographic record
Abstract
Australia has had two recent public apologies, one to the ‘ Stolen Generation’ of Aboriginal and Torres Strait Islander Australians and the second to the ‘Forgotten Australians' – people who had been removed from their parents as children and institutionalized. Both acts occurred in time when there was no Internet and peoples’ stories took years to collect and decades for their weight to carry the public momentum required to gain a public apology. Now, in a digital age, the reports and the testimonies held within them are available for all to read on the Internet. We all now know what happened and formal public apologies ensued. Both public apologies also draw attention to an emerging intersection between digital technologies, personal historical stories and public apology. Research has identified the potential of digital narrative, such as digital storytelling3 and videoed oral histories to assist in the production of digital narratives that can help to present the multiple voices and viewpoints of those affected by these subjects co-creatively (Burgess et al, pp.152-153). Not all Australians however have access or the skills to use digital tools so as to benefit from these technologies ⎯ especially Indigenous Australians. While the Federal Government is committed to helping Australians enjoy digital confidence and digital media literacy skills, experience inclusive digital participation and benefit through online engagement (Department of Broadband, communications and the Digital Economy, 2009) there are many initiatives that can also be undertaken locally by State funded institutions, such as libraries to assist. This paper highlights the outcomes of recent empirical projects undertaken at the State Library of Queensland (SLQ) in particular focusing on digital initiatives in Family History practices by Indigenous users, and a digital story project in response to the public apology to the Stolen Generation instigated by SLQ.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".