Bibliographic record
Abstract
Canadian forest and fisheries policy networks were early adopters of certification. At both the federal and provincial levels, certification presented policy networks with significant strategic threats and opportunities. With respect to forest certification, the FSC was perceived as a direct threat to network interests which immediately responded by establishing an alternative, national forest certification scheme. Matters were different with respect to fisheries certification, with the MSC endorsed and promoted albeit not especially enthusiastically. In this chapter, we examine the development of forestry and fisheries certification in Canada, focusing on the actors involved in promoting and blocking it. We highlight the peculiarity of the trajectory of the FSC and the MSC schemes in the country. While the FSC received almost no support from industry and government at the outset, by 2010 it was surprisingly well placed, accounting for over 30 per cent of total area certified and growing faster than other schemes. In contrast, the MSC, which had been relatively well received at the outset, was coming under increasing criticism for the complexity of its certification processes and the environmental and social weakness of its standard. To understand these dynamics, we first examine the evolution of the FSC, followed by the MSC. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".