Examining the <i>Specifics</i> approach to identifying indicators of sustainable natural resource management in Ontario, Saskatchewan, and British Columbia
Bibliographic record
Abstract
Natural resource managers, environmental interest groups, and public agencies need identifiable, measurable indicators of sustainability based on meaningful fine-scale specifics that are appropriate for both fine and increasingly broader social/ecological scales. The "Identify the Specifics" framework, field tested in Ontario, Saskatchewan, and British Columbia, uses collective local expert knowledge to integrate and prioritize social/ecological concerns that become the foundation for both local and increasingly broader-scale indicators of sustainable management. Results to date suggest that: (1) local experts have valuable knowledge to contribute; (2) identified local indicators, once reviewed, can contribute to both local- and broader-scale indicators; (3) fewer than 10 indicators may provide an adequate foundation for assessing the sustainability of local range and forest management practices; and (4) local and broader-scale experts commonly identify different indicators because they have different knowledge bases, priorities, and responsibilities. Differences in the indicators identified among experts representing different scales may be minimized if indicators at broader scales are developed with knowledge of specifics from finer scales. The Specifics approach is presently being used across British Columbia to help identify knowledge gaps and related research and extension priorities. Key words: criteria and indicators, ecological concerns, extension, forest management, natural resources, priorities, range management, specifics, sustainability
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".