Outdoor recreation and forest management: A plea for empirical data
Bibliographic record
Abstract
For many people, outdoor recreation provides one of the main opportunities to experience, interact with, and learn about forested landscapes. Yet public recreation use of forests in Canada is not yet well understood; knowing more about this important forest stakeholder group would help to address aspects of social sustainability in forest management. Four considerations for explicitly addressing outdoor recreation interests in forest land-use planning and for the collection of recreation data are presented: (1) normative; (2) pragmatic; (3) economic; and (4) governance. Approaches for the collection of recreation-use characteristics are also discussed. Key words: outdoor recreation, data collection, sustainable forest management, social values
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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 | 0.135 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".