Towards an Australian Digital Communications Strategy: Lessons from Cross-Country Case Studies
Bibliographic record
Abstract
In the early 21st century, governments developed national broadband plans to supply high-speed broadband networks for the emerging digital economy and to enable digital services delivery. Most national broadband plans are now focused on moving to ever faster networks, but there is a growing need to develop national digital communications strategies to focus on the demand-side of the broadband "eco-system". In this paper, we outline the approaches adopted by the United States, Canada, the United Kingdom, Singapore, and Korea to assist in the development (or renewal) of Australia's national broadband strategy, or, as we prefer, national digital communications strategy. The paper draws on the lessons learned from the case-study countries and the recent pandemic and considers some theoretical aspects of the broadband ecosystem. We conclude by suggesting a process to re-evaluate Australia's national digital communications strategy as it rolls forward, and to incorporate recent international trends to develop demand-side policies to enable greater adoption and use of existing broadband infrastructure and digital services.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".