Relevance of social science to the management of natural resources in British Columbia
Bibliographic record
Abstract
Ecosystem-based natural resource management involves the integration of biophysical and human dimensions. Both the social sciences and biophysical sciences contribute to our understanding of the process of balancing social, economic, and biological factors. While the role of the biophysical sciences is relatively well recognized in the natural resource management sector, the contributions of the social sciences are less well understood and they are less frequently incorporated into management plans and activities. In this paper we summarize several distinct contributions of the social sciences to natural resource management and describe 10 ways that decision makers use social sciences. We predict the role of social sciences in natural resource management will become more important and we suggest that more collaborative research projects between social science researchers and natural resource managers will emerge. We also suggest that more cross-fertilization within the diverse streams of social sciences— as well as between the social sciences and biophysical sciences—will be essential in order to address complex research questions related to natural resource management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".