Multidisciplinarity, interdisciplinarity and training in forestry and forest research
Bibliographic record
Abstract
The nature of forestry is changing rapidly, with the social component becoming as or even more important than the traditional biophysical components. The role of participatory approaches to forestry has increased dramatically, and meeting the needs of people is now seen as a primary function of forestry. Increasingly, those needs are being defined through bottom-up approaches, rather than by governments or corporations. Foresters and forest scientists are poorly equipped to deal with this change, which is necessitating a much broader knowledge than has previously been required. At the undergraduate level, forestry programs are failing to teach the skills necessary for successful participation in this new form of forestry. At the graduate and post-graduate levels, young scientists are particularly disadvantaged, as the conservative nature of the academic system can actually work against attempts to be more interdisciplinary and more relevant. Scientists who are genuinely interdisciplinary may have difficulties finding employment, and current academic reward systems do not cope well with individual contributions to team efforts. The problem extends to the forestry profession, with many professional foresters being ill-equipped for their new roles, while at the same time they and/or their employers remain reluctant to enter into any form of re-training. Key words: university education, graduate training, interdisciplinarity, multidisciplinarity, pedagogy, forestry training, forestry paradigms
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".