Bibliographic record
Abstract
Ensuring the protection of natural environments is vital for the prosperity of future generations. In all forested regions of the globe, local traditions have been essential in creating healthy human-forest relationships. These practices can be summed up as Traditional Ecological Knowledge (TEK), forming an increasingly important factor to consider when developing practical and effective forestry policy. Despite the international community’s gradual acceptance of TEK as a valid policy guide, the field of forestry in many regions of the world continually ignores this crucial concept. As such, many forest ecosystems are harvested in ways that do not reflect the values of local - and often indigenous - communities, resulting in socio-economic divisions and unsustainable environmental degradation. This paper examines the state of TEK in the forestry sector through conducting a comprehensive literature synthesis of thirty-six published papers. Based on trends within the selected literature, the majority of articles identify TEK as providing social, political and economic benefits to the forestry sector. The literature also indicates that governmental and non-governmental forestry actors have and continue to neglect TEK as a policy tool, with 72% of the works examined directly discussing the damaging effects of this trend. The implications of these results are discussed in the light of temporal issues relating to the forestry sector, providing an impetus for both academics and leaders in forestry to consider the importance of TEK in policy and research.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".