Current status and future directions of traditional ecological knowledge in forest management: a review
Bibliographic record
Abstract
In the last 25 years, the number of published studies that refer to traditional ecological knowledge (TEK) has constantly increased, with now more than 200 papers published each year. The objective of this review was to determine how this knowledge is used in current forest management around the world, and how local communities are involved in forest management planning. Published papers from 1983 to 2005 relating to TEK were found using the ISI Web of Science database. Despite the growing amount of literature published on TEK, we found only 21 studies that specifically address forest management per se. In these studies, TEK integration took different paths: using traditional management rules as a framework (five studies), using value maps to adapt practices in time and space (three studies), or by a zoning process that divides the land into areas in which different land uses are emphasized (six studies). Some community involvements are “active” with co-management committees composed of stakeholders (including community members), each having a voice; some are “passive” with external managers using criteria and indicators previously developed from community values and objectives. Although important changes in mentality and firm political decisions are still required before more efficient partnership between TEK and western science is reached in forest management planning, our review showed that Canadian initiatives proposed promising processes that could ensure better TEK incorporation and improved community participation. Key words: traditional ecological knowledge, TEK, sustainable forest management, community participation, first nations, forest management planning
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".