Impacts of locally situated R&D in forest industry : a comparative case study between Joensuu, Finland and Prince George, Canada
Bibliographic record
Abstract
The object of this thesis was to explore different factors of how locally situated Research and Development (R&D) institutes shape the forest industries in Joensuu, Finland, and Prince George, Canada. Another object was to explore what kind of role the local governments have on accepting the new forestry products relating to bioeconomy. The aim was also to discover underlying factors of how Joensuu has succeeded leveraging the industry as becoming the “European forest capital”. Through comparative analysis in socio-political aspects the study analyses government’s role and the social acceptance in the communities. The thesis showcases different projects implemented in the city related to wood construction and bioenergy as a measure of support from the city to the forest industry. \n \nThe study discovered how regional policies in Finland have affected positively the forest industry in Joensuu, enhancing cluster development, and locating important research facilities in the city. Large investments and the local governments’ active role in enhancing the local knowledge networks and implementing strategies and programs related to forest-based bioeconomy have influenced the industry to gain recognition internationally. It was also evident that the presence of R&D institutes are attracting investment and expertise in the region. Comparing the industry in Prince George, it is evident that these two industries are differing in terms of research capacity and focus, and how local governments are supporting the industry by different strategies, policies, and programs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".