A Submission on the Discussion Paper, A Place for Innovation: Queensland Innovation Places Strategy
Bibliographic record
Abstract
EXECUTIVE SUMMARY Recommendation 1 The Queensland Government should renew its Queensland Hackerspaces Grant program to further support makerspaces, fab labs, and hackerspaces in Queensland. Recommendation 2 The Queensland Government should expand its existing Advanced Manufacturing Hubs, and add new Advanced Manufacturing Hubs to its network. The network of manufacturing institutes in the United States could be a useful model for this expansion. Recommendation 3 The Queensland Government should further expand and diversify its range of specialist Innovation Hubs. Moreover, the Queensland Government should develop Innovation Precincts – bringing together government, industry, and higher education to work on specialist fields of science and technology. Recommendation 4 The Queensland Government should also support local government initiatives in respect of Smart Hubs. Recommendation 5 The Queensland Government should support the development of new innovation spaces, places, and precincts at Queensland universities. Recommendation 6 The Queensland Government should also take note of the successes and failures in respect of commercial innovation spaces and places. Recommendation 7 The Queensland Innovation Places Strategy should include libraries (and other cultural institutions) as key innovation places and spaces. Recommendation 8 The Queensland Innovation Places Strategy should also foster social enterprises and community innovation. Recommendation 9 The Queensland Innovation Places Strategy should establish a network of innovation centres to protect, promote, and enforce Indigenous intellectual property, innovation and knowledge. Recommendation 10 The Queensland Innovation Places Strategy should be compared with the innovation policies and practices of other Australian states and territories. Recommendation 11 There is a need for better Federal Government support of research, innovation, and science. As the Productivity Commission has observed, there should be a better co-ordination, integration and networking of innovation initiatives in Australia – so that they can scale up, and be internationally competitive. There should also be a revisiting of the Northern Australia agenda. Recommendation 12 The Queensland Government should draw lessons from innovation policies and practices in other key jurisdictions – such as the United States’ Advanced Manufacturing Policy; Canada’s Innovation Superclusters Initiative; and the European Union’s Strategy on Research and Innovation. The Queensland Government should also consider how its innovation policies and practices will be affected by international trade and investment agreements (bilateral, regional, and multilateral). Recommendation 13 The Queensland Government should explore the participation of Queensland innovation places and spaces in international technology mechanisms (for instance, the UNFCCC Climate Technology Centre and Network; the UNDP Accelerator Lab Network; and the ACT-Accelerator). The Queensland Government should also seek to include Queensland innovation places and spaces in ‘Big Science’ projects.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.243 | 0.072 |
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".