Emergent Imaginaries and Fragmented Policy Frameworks in the Canadian Bio-Economy
Bibliographic record
Abstract
Climate change means that countries like Canada need to find suitable transition pathways to overcome fossil-fuel dependence; one such pathway is the so-called ‘bio-economy’. The bio-economy is a term used to define an economic system in which biological resources (e.g., plants) form the basis of production and production processes. For example, it would involve the replacement of petroleum energy, inputs, chemicals, and products with bioenergy, biological inputs, bio-chemicals, and bio-products. A number of countries and jurisdictions have established policy strategies in order to promote and support the development of a bio-economy, exemplified by the European Union where the bio-economy represents a key pillar in its broader Horizon 2020 strategy. Other countries, like Canada, do not yet have an over-arching bio-economy strategy, but have a series of diverse, and often competing, policy visions and frameworks. It is useful to analyse countries like Canada in order to understand how these policy visions and policy frameworks are co-constituted, and what this might mean for the development of an over-arching bio-economy strategy. This raises a number of questions: How is the bio-economy imagined by different social actors? How are these imaginaries and policy frameworks co-produced?
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.052 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".