Modern Biotechnology in New Zealand: Further Analysis of Data from the Biotechnology Survey 1998/99
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The New Zealand Government has indicated a strong interest in fostering innovation and aims to concentrate on selected areas where New Zealand may be able to develop a new comparative advantage. One such area is biotechnology, which would build on New Zealand's existing comparative advantage in the primary sector dairy, forestry, meat, wool and horticulture). This paper aims to fill some of the gaps in our knowledge of biotechnology and innovation processes in New Zealand. It is based on the 1998/99 survey of modern biotechnology activity in New Zealand conducted by Statistics New Zealand in 2000. The survey was commissioned by the Ministry of Research, Science and Technology (MORST) mainly in order to produce statistics on the present position of the industry for planning purposes. The findings reported in this paper are based on further analysis of the survey data conducted by the author on behalf of MORST. Data are presented on the number, type and characteristics of enterprises involved in biotechnology in New Zealand. The paper presents data on enterprises that conduct R&D into modern biotech processes and includes analysis of the rate of innovation by biotech respondents compared to OECD estimates. Comparisons are also made between data from the New Zealand and Canadian biotech surveys.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it