A systematic map of knowledge exchange across the science‐policy interface for forest science: How can we improve consistency and effectiveness?
Bibliographic record
Abstract
Abstract Knowledge produced by scientists is essential to the policy and practice of managing natural resources, including forests. However, there has never been systematic mapping of which techniques in knowledge exchange (KE) have been applied in the forest sciences, by whom, and to what effect. We examined KE techniques documented in the forest sciences globally. We used standardized search strings in English and French across two academic search engines (BASE and Scopus) and a specialist website (ResearchGate) to locate relevant items. We screened items, extracted data, conducted qualitative and quantitative analysis, and built a network visualization diagram to demonstrate knowledge flow. Our final map included 122 items published from 1998 to 2020, with most published after 2010. Items mentioned organizations from 66 countries as knowledge producers or users. The interactive network visualization diagram displays linkages between organizations, sectors and countries. We found that most of the KE activity involved the Global North (89%). Governments were the most common knowledge users, and industry was frequently reported as a user but rarely a producer. Academia was both producer and user. Indigenous, local, traditional or community knowledge was included in 24% of items, but these communities were not associated with any coauthor affiliations. Reported funders were universities, governments, non‐profits or foundations. We found 90 unique terms in the items related to KE with less than 25% of terms used in more than one item. Fifteen per cent of item keywords related to KE. The most commonly identified enabling conditions for KE were trust, funding and established relationships, while major barriers were challenges for translation of science and lack of time. To improve searchability of information related to KE and encourage a culture of considering KE in scientific research and forest management work, we recommend a common lexicon of ‘knowledge exchange’/‘échange de connaisances’. We recommend that more effort be given to forest science‐related KE connections between the Global North and South as well as a deliberate collection of evidence for the effectiveness of KE techniques. Researchers and practitioners can use our KE typology to identify their goals and design appropriate evaluation measures.
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 | 0.137 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.073 | 0.073 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| 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".