Bibliographic record
Abstract
New Zealand’s remarkable transformation from a wool and meat producer to a highly diversified economy is one of the more remarkable economic stories of the post-World War II period. Part of this diversification is tied to New Zealand’s development as a world-class wine producer, a remarkable feat given its small population. New Zealand’s institutional arrangements provide an example for other small agriculturally-based producers wishing to move to higher value-added production. To supplement the existing literature, mail surveys, phone and Skype interviews were carried out by the authors in spring and summer 2012. In addition, the authors held several informative discussions with local experts during the AAWE Conference in Stellenbosch in summer 2013. Experts came from academia, industry and government, as one would expect with a study on the Triple Helix model. Several agreed to review the document for factual accuracy, though the interpretations are solely those of the authors. While New Zealand’s institutions support the basic premise of the Triple Helix framework, that is, of the need for coordination of research, production and policy efforts, there are some important additional elements that are noteworthy for other small producers. Niche specialisation around a long-term strategy and a limited but strategic role for government are important, but the more remarkable feature is the ability to harness multinational investment towards local development. Yet, as we discuss, such approaches also carry with them their own vulnerabilities, requiring further strategy adjustments on the part of firms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".