System Mapping to Explore Commercialization of Bio-Products in Canada's Forest Bioeconomy
Bibliographic record
Abstract
The commercialization of bio-based technologies in the Canadian forest bioeconomy involves consideration of socio-technical perspectives from policy, economics, ecology, and education, among others. There is a need to test and develop strategies for multidisciplinary learning at the graduate bioengineering level, so that researchers can position their technical innovations for more sustainable deployment. This thesis applies collaborative system mapping tools (namely actor mapping and causal loop mapping) within institutional teams to explore industry, policy, and research perspectives on biotechnology commercialization. Resulting maps were assessed using a conceptual framework of systems thinking to explore insights. These maps highlight missing roles from Ontario’s bioproducts industry network, suggest that policy incoherence between federal and provincial levels may lead to unexpected distribution of funding, and illustrate the impact of consumer perceptions on the resources available to researchers. Using system thinking tools allowed us to learn from different disciplinary perspectives by providing a structure to organize more holistic insights. That said, most of our findings are reported from the graduate researcher’s perspective. Inviting expert collaborators to participate in the mapping exercises is recommended as a next step to improve learning from generated system maps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".