The Ecosystem Dilemma: Discordance between Nature and Culture
Bibliographic record
Abstract
Following advances in thinking and practice made during the past 40 years in such diverse places as the Great Lakes, the Baltic Sea, and the Mediterranean, ecosystem-based management (EbM) concepts were applied in the Georgia Basin Ecosystem, on Canada’s west coast. In this article, we look back at the experience in the Georgia Basin Ecosystem and argue that, in the case of the Georgia Basin, natural ecosystem boundaries are not consistent with “cultural system” boundaries as illuminated by the concept of a sense of place. This discordance may be a fundamental reason why application of EbM approaches in the Georgia Basin Ecosystem has had little acceptance by policy makers, politicians, and the public. The key lesson is that where such discord exists, EbM approaches may be less effective. Conversely, when ecosystem and cultural boundaries are aligned, an EbM approach can be expected to be embraced more readily.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".