Assessing integration in resource and environmental management in the Southwest Yukon
Bibliographic record
Abstract
This thesis investigates the theory and practice of integration in resource and environmental management. Research on integration is growing as a result of the escalating demands placed on resources, an increase in the quality and quantity of information available concerning the environment, and the increased involvement and coordination of partners and participants in resource and environmental decision-making. Focus is placed on several resource and environmental decision-making. Focus is placed on several resources and environmental management processes in the Southwest Yukon, including wildlife management, protected areas management, forest management and environmental assessment. A case study approach is utilized to examine the perception and practical application of integration in these processes, and to guide the collection of relevant qualitative evidence through documentation and open-ended interviews. A conceptual framework built around the existing integration literature has shaped and directed the analysis of this study. The conceptual framework identifies opportunities for and practical applications of integration. However, experience from the Southwest Yukon suggests that the current definition of integration requires refinement. Factors affecting the successful implementation of integration, including communication, politics, time and capacity, are also discussed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".