Crossing disciplinary boundaries in forest research: An international challenge
Bibliographic record
Abstract
It is generally recognized that improving our understanding of forest-related research problems will involve amalgamating knowledge and methods from different disciplines. The presence of complex values within complex systems has persuaded many scientists engaged in forestry-related research to begin exploring cross-disciplinary paradigms in order to transcend the limitations of traditional disciplinary thinking. It has been suggested that authentic interdisciplinary programs in the sciences remain rare and that academic departments, academic supervisors and funding agencies present the main barriers to effective cross-disciplinary research among scientists. Despite these barriers, scientists around the world are increasingly approaching their research problems from a cross-disciplinary perspective to provide meaningful solutions to complex environmental problems. Key words: cross-disciplinary, interdisciplinary, forest research, complexity
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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 | 0.110 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".