Why Governments Invest in the Innovation of 5G Technology: a Closer Look at the Australian 5G Innovation Initiative
Bibliographic record
Abstract
At the forefront of the digital revolution, 5G technology is transforming industries and infrastructure and generating benefits that extend well beyond the realm of telecommunications.However, the innovation and commercialization of 5G technology introduce risks.Therefore, it is interesting when governments choose to support the innovation of 5G technology in the private sector.This paper examines why, in light of the tensions with Huawei, the Australian Government has chosen to invest in the innovation of 5G technology through the Australian 5G Innovation Initiative.Guided by an original theoretical framework, the paper conducts a thematic analysis of how the Australian Government chose to describe, design, and execute the Australian 5G Innovation Initiative.The findings suggest that the Australian Government invested in the innovation of 5G technology through the Initiative to strengthen key sectors of the national economy, improve public infrastructure, and facilitate the provision of public goods.Proving an efficient means of analyzing the Government's motives, the theoretical framework can be applied to other studies of government intervention in technological innovation.However, the theoretical framework failed to account for the role of public infrastructure and intra-governmental collaboration in Australia's intervention behaviour, reflecting an important gap in the literature on which it is constructed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".