First Nations Digital Inclusion in Western Sydney
Bibliographic record
Abstract
This report showcases the findings of the First Nations Digital Inclusion in Western Sydney project—a partnership between the Whitlam Institute, Western Sydney University researchers and community organisations. The project which was overseen by an Indigenous Governance Committee draws on an online survey, yarning circles, and storytelling interviews. This report amplifies the day-to-day realities of Indigenous peoples in Western Sydney, revealing how inequalities in the digital space disadvantage Indigenous peoples in one of Australia's most diverse and growing urban regions. Digital inclusion is fundamental to equitable participation in society—affecting all aspects of life including access to education, employment, healthcare, and government services. Access to technology assists Indigenous peoples in Western Sydney to stay in contact with family and friends, to share knowledge about their culture, kinship, and Country. Yet, Indigenous peoples in Western Sydney continue to face significant barriers in accessing reliable digital technology, affordable internet, and digital literacy opportunities. While digital inclusion is recognised as a key priority under Outcome 17 of the National Agreement on Closing the Gap, current efforts fall short in addressing the challenges faced by Indigenous peoples in urban settings. This report presents a comprehensive analysis of these barriers and outlines solutions driven by Indigenous peoples and their experiences.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".