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Record W7135326625

Defence Industry in National DefenceRethinking the future of Australian defence industry policy

2023· other· en· W7135326625 on OpenAlexaboutno aff
Stephan Fruehling, Kate Louis, Jeffrey Wilson, Graeme Dunk

Bibliographic record

VenueANU Open Research (Australian National University) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDefence industryContext (archaeology)Government (linguistics)Defense industrySovereigntyStrategic defenceNational securityCompetition (biology)
DOInot available

Abstract

fetched live from OpenAlex

As our geostrategic environment deteriorates, the Australian Government has adopted the concept of National Defence – the defence against potential threats arising from major power competition – as a new approach to defence planning and strategy. While many reforms will be required to implement the National Defence concept, building Australia’s defence industry capability is one of the most important. The Defence Strategic Review has argued for the need to build enhanced sovereign defence capabilities in key areas. However, the current paradigm of defence industry policy was established in a very different context to that of today. Risks of major power conflict were low, policy assumed a 10-year warning time, and industry capability was viewed largely in terms of supporting individual ADF programs. This report examines the role of defence industry in the context of Australia’s National Defence strategy. It argues that a change is required to recognise defence industry not as an input to capability but as national capability in its own right. The possession of a sovereign but internationally linked defence industry is itself an asset during a period where the risk of major conflict is rising. To inform the national debate in Australia, this report examines defence industry policy in five countries: Sweden, France, the UK, Israel and Canada. These case studies offer pertinent lessons for how defence industry policy can be implemented in different strategic contexts. The report identifies several factors that shape effective policy: fostering defence-civilian industry embeddedness; utilising a broad range of industry policy tools; ensuring formal and informal coordination between government and business; balancing competition and strategic relationships; and leveraging international markets for scale. The report then connects these lessons to Australia, considering how our defence industry policy could be reformed to deliver on the needs of a National Defence Strategy. It offers five recommendations for the future of defence industry policy in Australia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.007
Scholarly communication0.0150.009
Open science0.0010.006
Research integrity0.0120.008
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.198
GPT teacher head0.425
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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