Managing karst in Coastal British Columbia, Canada : systems and implementation results
Bibliographic record
Abstract
This thesis provides a detailed description and analysis of the system used for managing karst in the forests of coastal British Columbia (BC), where the major land- use activity is industrial forestry. In 2004, BC shifted from a more prescriptive forest management model (the Forest Practices Code) to a less regulated, results-based approach based primarily on the Forest and Range Practices Act (FRPA). The dissertation examines how this results-based management approach for forest resources has worked – or not worked – with respect to karst. The research focused on the five key realms of an environmental management system as defined by the International Standards Organization (ISO 14001 standard): 1) Legislation and Policy, 2) Planning, 3) Implementation, 4) Checking/Corrective Action, and 5) Management Review. The research also examined the roles of professional reliance (another major foundational element of the FRPA model), karst research, and organizational capacity as external factors which influence on the functioning of the karst management system. A combination of interviews, surveys, document reviews and field observations were used to collect qualitative information relevant to all aspects of the karst management system. This research reveals inadequacies in all five realms of BC’s current karst management framework, including gaps in legislation, a lack of implementation of existing standards and guidelines, and non-existent effectiveness and compliance monitoring. The results suggest that BC’s shift to ‘self-regulation’ has yielded unsatisfactory results so far for karst resources and has hindered progress toward implementing a fully integrated science-based ecosystem approach to karst management in the study area. Professional reliance failures are identified as one of the key factors contributing to a breakdown of the management system for karst. This is the first comprehensive study that examines the systems and processes used for managing karst in coastal BC, and consolidates knowledge for government, industry, and others that wish to study or better understand BC’s approach and methods for managing karst. The findings will be useful for private and public forest sector organizations endeavoring to implement fully-functional and effective systems for managing karst in a forestry context. This information may also have more specific applications for managing karst.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".