Title: Food Chains and Funding: Value Chain Development and Roles for Governments Paper Proposal for either the Food Chain Approach or Agribusiness Strategies streams
Bibliographic record
Abstract
agriculture and food policy. David has also been president of a farming company, an agri-business insurance company and a biotechnology start-up. David is a Senior Associate at the University of Melbourne and has taught at the Australian Graduate School of Management in Sydney. His research interests are in agri-food supply chains, food policy particularly related to organizational and technical innovation. David has over 60 papers and technical reports, 150 academic and industry presentations and 140 media appearances related to agriculture and food. His research also included commercialization of new technologies and he has studied biotechnology and bioproduct innovation in Canada and Australia, looking at the development of knowledge chains in these industries. Glen Snoek is a recent MSc graduate from the University of Guelph. His research was in food value chains, the roles of government funding and the factors affecting performance of the chains. He is now a farm policy analysis with the Canadian Federation of Agriculture. Glen has extensive industry experience working for input suppliers to Canadian farm businesses. Food Chains and Funding: Value Chain Development and Roles for Governments Problem Statement: Agriculture and food companies are always searching for new product
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".