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Record W2091995661 · doi:10.1111/1746-692x.12017

Wheat Research Funding in Australia: The Rise of Public–Private–Producer Partnerships

2013· article· en· W2091995661 on OpenAlexaff
Julian M. Alston, Richard Gray

Bibliographic record

VenueEuroChoices · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRevenueMultinational corporationBusinessProfit (economics)Agricultural economicsFinanceEconomics

Abstract

fetched live from OpenAlex

summary Wheat Research Funding in Australia: The Rise of Public–Private–Producer Partnerships The Australian wheat research system was transformed profoundly by three institutional innovations. First, in 1990 the Grains Research and Development Corporation (GRDC) was created to provide levy‐funded R&D. Second, in 1994 the Plant Breeder’s Rights Act was passed, providing the legal framework for the collection of end‐point royalties (EPRs), now the primary source of funding for wheat‐breeding activities in Australia. Third, in 1999 the GRDC tendered for the development of three for‐profit public corporations that would invest revenues from EPRs to fund wheat breeding, allowing the GRDC to move upstream to focus on using its levy‐based funding for pre‐breeding research efforts. As of 2012, these breeding firms had each acquired a multinational private partner and had collectively reached the point where EPR revenues were sufficient to cover breeding costs. EPRs provide a very strong form of property rights for breeders such that producers will have to pay higher prices to access improved varieties, with some uncertainty about the extent to which those prices will be free to rise. Together, these three institutional innovations have created a well‐funded and well‐coordinated wheat research system that will enhance Australia’s long‐run competitive position in the global grain market, but with an increasing burden of the costs of innovation borne by the industry.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.357
GPT teacher head0.353
Teacher spread0.004 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations18
Published2013
Admission routes1
Has abstractyes

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