The Deployment of Broadband Internet in Australia: Areas for Attention and Implications from Canada and Korea
Bibliographic record
Abstract
Broadband Internet connectivity is currently seen as a means to increase the efficiency and competitiveness of an economy. The deployment and use of broadband capabilities is high on the current political agenda in many developed and developing countries, including Australia. But despite ongoing efforts to promote broadband in Australia, deployment has been much slower than expected. This paper aims to identify areas that have been holding up the broadband development in Australia. In examining four areas for attention (demand, competition, price and the role of government), we refer to experiences in Canada and Korea, both leaders in broadband deployment, to show the differences in each area. Although each country discussed here has its own policy agenda and some unique circumstances related to broadband deployment, implications from this paper will provide valuable input for policy makers and industry leaders in Australia (and elsewhere) as they develop strategies to encourage more widespread broadband deployment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".