Science and technology development and the depoliticization of the public space
Bibliographic record
Abstract
Purpose The authors aim to review a five‐year multi‐study research programme on the role of public dialogue in the social and cultural sustainability of biotechnology developments in New Zealand. Design/methodology/approach The authors conducted a critical review of all the published research products from a five‐year government‐funded study of the cultural and social aspects of sustainable biotechnology in New Zealand. Findings The review research highlights how New Zealand Government policies on biotechnology, which motivated the research programme, were fore‐grounded on economic progress and competitive positioning. Thus, debate on sustainable biotechnology issues became cast in economic and technical terms, while public dialogue became seen as diversionary and unsubstantiated. The analysis concludes that the programme was ineffective in influencing government policy and fell victim to the very problem of science governance that its purpose was designed to address. Research limitations/implications The research develops implications regarding the ability of government‐funded sustainability research to influence policy. Originality/value The review focuses on the purpose, content, outcomes, and context of the research programme and identifies a number of key themes that arose from the programme that are useful for other sustainability policy researchers. The reviewers conclude that this case demonstrates that the marketization of the public sphere depoliticises the social and cultural construction of the nation's future.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".