Forest bioeconomy in Ontario – A policy discussion
Bibliographic record
Abstract
Ontario’s forest sector has been undergoing a significant structural shift resulting from a more than decade-long trend of declining markets for traditional products. Though there have been signs of industry recovery, the forestry industry is still far smaller than it once was. In order to sustain and improve Ontario’s economic position, we must develop policies and supporting programs that transition our forest economy to a more robust and diverse set of markets, including capitalizing on opportunities that come from a thriving and sustainable forest bioeconomy. The current suite of programs is fragmented and does not provide support for all types of bioproducts and policy initiatives, nor across all sections of the value chain. These factors, coupled with the lack of a clear strategic direction for the bioeconomy, have contributed to Ontario’s slow emergence into the bioeconomy when compared to other jurisdictions. It is proposed that a key way to improve the effectiveness and efficiency of policy support for the emerging bioeconomy is to integrate and coordinate the actions of the government with industry and academia players involved in the bioproducts sector. Forest policy needs to take a balanced, integrated approach to promote all aspects of the bioeconomy to help ensure the eventual success of Ontario’s forest bioeconomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".