The promotion of ‘innovation’ in forestry: a role for government or others?
Bibliographic record
Abstract
The forest sector is perceived as being traditionally conservative and reluctant to adopt changes, even when these can be beneficial. This is a natural consequence of an industry that tends to operate over relatively long time periods. However, to compete successfully in a rapidly globalizing world, the forest sector will need to be much more innovative than it has been to date. Government policies could play an important role in encouraging innovation in the forest sector yet, despite many initiatives, they have had variable success in doing so. It is likely that transformative innovations will occur that involve different actors to those dominating the sector today, and there is already evidence of such innovations taking place. Transformative innovation may not, however, be in the short-term interest of governments that have used the forest sector to encourage rural development. Other innovations may be stimulated as the crises facing the forest sectors in individual countries deepen. However, it is uncertain whether these will ensure that forests fulfil their potential in enabling the development of a global bioeconomy. To do so, effective partnerships between all the interested parties are needed: government, industry, academia, and non-governmental organizations.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".