Bibliographic record
Abstract
An innovation system not only depends on the underlying scientific and technical bases of a given sector, but also on the characteristics of the environment in which the innovative organizations evolve, the linkages among innovation actors and the inner capacity of these organizations to innovate. In early 2001, a study was undertaken by the Conseil de la recherche forestière du Québec for the purpose of describing the innovation system in Quebec's forest sector. This was achieved by assessing 33 indicators using a database detailing the research projects underway in 19992000 in academic, governmental and private research centres in Quebec. This assessment showed that Quebec has many programs that provide training in natural sciences, yet programs focusing on forest sciences attract very few Quebec students. The assessment also showed that Quebec does particularly well in terms of infrastructure when it comes to forest-related research. More than 1300 scientists, research professionals, technicians and graduate students work in some twenty establishments, where $120 million is spent on research in the areas of forest management and resource processing. Furthermore, the assessment showed that there are numerous ways that innovation actors can interact with one another. Popular initiatives include meetings among interested parties to define research priorities, the development of joint research projects and the pooling of research resources. However, mechanisms encouraging the mobility between research centre personnel and organizations involved in forest management or the processing of forest resources are still very scarce. Key words: forest research, innovation system
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".