Transition to a Bio-economy: A Community Development Strategy Discussion
Bibliographic record
Abstract
Many jurisdictions are questioning existing practices in making effective use of forest resources. Even in Europe, considered one of the more advanced in value-added production, Engelbrecht (2006) identified that European companies by and large produce low value-added products and that innovation could help them to make more of their environmental advantages. Schaan and Anderson (2002) categorized the forest sector system opportunities into innovations around forest management, harvesting, primary manufacturing, services, and manufacturing suppliers. They found, as did Wagner and Hansen (2005) that firms in forest harvesting and primary manufacturing tend to concentrate on process innovation rather than the development of new products. As a result, forestry industry cutbacks in employment are hardly surprising. Clearly, future job growth will need to come from elsewhere, and many from the forest, which is still considered as holding a wealth of resources and opportunities. Ontario (Canada) and perhaps other similar jurisdictions have a number of communities reliant on the forest economy, most of which suffered severe cutbacks. These communities are beginning to feel the need to diversify and encourage innovation. Although not a panacea to their problems, the bio-economy provides some opportunities worth investigating including a more thorough use of forest products. This article adopts an economic development approach and explores the challenges in getting involved in the bio-economy, it offers a list of opportunities, and a framework to analyze challenges.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".