Increasing partnerships between scientists and forest managers: Lessons from an ongoing interdisciplinary project in Québec
Bibliographic record
Abstract
Adaptive management presupposes stronger links between scientists and forest managers in order to adapt research processes and findings to production activities. Partnerships between these two groups are starting to emerge in the forest sector in Quebec. However, local forest managers have not always had the occasion in the past to contribute to research processes. Moreover, scientists have not always had the opportunity to harmonize all their respective research projects at the local level. This research project was thus aimed at establishing a link between local forest managers and scientists in order to direct research projects towards local needs and concerns. The purpose of establishing this contact between local forest managers and scientists was to create opportunities for inter-disciplinary research projects. This experiment demonstrated that the roles and attitudes of scientists and forest managers still need to evolve in order to increase the chances for successful partnerships between these two groups. On the one hand, forest managers need to view research (1) as part of their daily activities and (2) as bringing benefit in the long-term. On the other hand scientists must (1) invest time in understanding what the forest managers are doing and (2) consider forest managers as equal partners with useful knowledge and skills in developing the research questions and protocols. Key words: adaptive management, interdisciplinary research, collaborative learning, sustainable forestry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".