Assessing a Nation’s Competitiveness in Global Food Innovation: Creating a Global Food Innovation Index
Bibliographic record
Abstract
While food innovation is heavily influenced by the myriad of policies, regulations and other environmental factors within a country, globalization means that food innovation is also a matter of international competitiveness. This benchmarking exercise uses 24 variables to compare the different innovation environments across ten countries: Canada, the US, Mexico, the UK, France, Germany, Italy, the Netherlands, Japan, and Australia. Quantitative and qualitative data was collected from publicly available sources only to measure each variable and ultimately provide a ranking. Qualitative data was evaluated using thematic coding to establish baseline practices and then compare each country to the baseline. Quantitative data was evaluated by constructing an average to which each country was compared. Countries whose data showed they met the average were awarded two points, and those who performed above or below average were either awarded an additional point or saw a point deducted. A final ranking was established from the scores across all four pillars, and the ranking was weighted to account for lacking data. The final weighted ranking saw the UK rank first, followed by the US, Germany, Australia, Canada, the Netherlands, Japan, Mexico, France and finally, Italy in tenth place.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".