Information technology and indigenous communities
Bibliographic record
Abstract
This document sets out key issues identified in the final plenary session at the AIATSIS research symposium on information technologies and Indigenous communities. Over 70 papers were presented at ITIC on the use of information technologies by Indigenous peoples. Illustrating the strength and vibrancy of the sector, presentations were delivered on programs, projects and research being implemented and undertaken by a range of community organisations,institutions and researchers across Australia. ITIC demonstrated the growing presence of an impressive and exciting IT sector in which digital media is being used in diverse and creative ways by Indigenous Australians to support, for example, innovation, employment, training and governance, as well as the production, maintenance and transmission of culture. The sector builds on over 30 years of cultural and social capital in IT and Indigenous communities. The use of digital media was showcased in a range of programs and initiatives spanning education, language, health and wellbeing, local and national digital archiving repositories, and the burgeoning creative industries and broadcasting sectors. The symposium highlighted the ability of IT to generate unique opportunities for income generation and local enterprise development. In particular, ITIC demonstrated the key capacity of IT to engage young people, particularly in creative media, thus providing new platforms for formal and informal training to support personal and career development. Overall, the symposium revealed not only the extent and variety of services already provided through IT by Indigenous people for the communities (both Indigenous and non-Indigenous) in which they live, but also the clear benefits arising from increasing engagement with digital media and the digital economy, and the potential for future growth. IT harnesses many crucial aspects associated with the economic future of Indigenous communities across the country.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".