Approaches to setting forestry research priorities: Considering the benefits of reducing uncertainty
Bibliographic record
Abstract
This paper reviews current approaches to research topic selection in forest management. Most current approaches are based on soliciting expert opinion of researchers within an environment where research demands may enter through media and political events. A number of potential problems are identified with these types of approaches including: research issues changing too rapidly for research programs to adapt, inability of surveys to capture long term trends in priorities, potential for processes to be captured by special interests of particular stakeholders, potential for consensus seeking to lead to research priorities that are too broad, a lack of statistical differences between topics leading to no significant priorities, a lack of explicit linking of the supply and demand for forestry research, and the potential for media to misrepresent research demands. Building on this literature, we suggest that current research selection methods be supplemented by developing new frameworks to provide more explicit information for research topic selection in forest management that would reduce the current pervasive role of subjectivity, provide research guidelines for local regions, and incorporate quantitative methods. This framework could be based upon the idea that one value of information is the reduction of costly mistakes. We suggest that sensitivity analyses on savings from potentially reduced uncertainty, within the context of different institutional constraints, could provide explicit information to assist with research topic selection. Key words: forestry research priorities, returns to research, value of information
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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 | 0.336 | 0.462 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".