Contribution of traditional knowledge to ecological restoration: Practices and applications
Bibliographic record
Abstract
Traditional knowledge has become a topic of considerable interest within the research and development environment. The contribution of traditional knowledge to conservation and management is increasingly recognized, and implementation endeavours are underway in several countries. The current scale of ecosystem degradation underscores the need for restoration interventions. It is increasingly recognized that successful ecological restoration depends on effective coordination of science and traditional ecological knowledge. This paper synthesizes the literature to evaluate the present and potential contribution of traditional knowledge to ecological restoration. Despite a growing number of articles published on traditional knowledge, only a few have addressed its contributions to ecological restoration per se. The main contributions of traditional knowledge to ecological restoration are in construction of reference ecosystems, particularly when historical information is not available; species selection for restoration plantations; site selection for restoration; knowledge about historical land management practices; management of invasive species; and post-restoration monitoring. Traditional knowledge and science are complementary and should be used in conjunction in ecological restoration projects. Incorporation of traditional knowledge can contribute to build a strong partnership for the successful implementation of restoration projects and increase their social acceptability, economical feasibility, and ecological viability.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".