Bibliographic record
Abstract
How can professional foresters ensure they practise with due diligence when they are charged with making decisions in which the advice of non-forestry specialists is one of the factors they must take into account? The case law on due diligence in Canada supports the need for appropriate expertise when potential regulatory offences are at stake. In a forestry context, the appropriate expertise is sometimes found among non-forestry specialists. As a result, professional foresters are growing increasingly reliant on the advice of such specialists. But especially in those provinces — BC, Ontario, and Quebec — with legislated exclusive professional forestry practice, professional foresters are often placed in the awkward position of having to judge the advice of specialists even when these foresters do not possess the expertise to make that judgment. Can foresters overrule the advice of non-forestry specialists? The case law on due diligence suggests they do so at their peril. By way of two hypothetical case studies, this paper highlights this dilemma, but also suggests that sharing the "decision-making space" may be a partial way to ensure that forestry decisions meet the demands of due diligence case law. Key words: professional ethics, professional forester, reasonable care, absolute liability, strict liability, R. v. Sault Ste. Marie
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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".