Measuring Digital Inequality in Australia: the Australian Digital Inclusion Index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past two decades digital inequality has come to be understood as a complex, evolving and critical issue in Australia, as it has elsewhere. This conceptual shift has generated demand for more complex measurement tools that can capture and combine multiple and graduated indicators of digital inequality. The Australian Digital Inclusion Index (ADII), developed in 2015 and now including annual data covering the period 2014 to 2018, is a composite index that addresses this demand. This paper describes the development of the ADII, its architecture and the dataset used to populate it. It also provides an overview of the findings of the 2018 edition of the index. The 2018 index reveals that, although on aggregate digital inclusion is improving in Australia, it continues to follow distinct geographic, social and socio-economic contours. In general, rural and regional Australians, older Australians and Australians with low levels of income, employment, and education are less digitally included than their compatriots. For some of these groups the inclusion gap is widening.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it