Recognizing Indigenous and Traditional Peoples and their identity, culture, rights, and governance of forestlands: Introduction to the Special Issue
Bibliographic record
Abstract
This Special Issue aims to provide an overview of the diversity of international research on the multiple ways in which Indigenous and Traditional peoples (ITP) are engaged in occupying and governing forest landscapes, consistent with their rights, values, knowledge and customs. This Introduction begins by reviewing our evolving understanding of two key questions: what rights are held by ITP; and how "Indigenous" and "Traditional" are actually defined. Papers in this Special Issue examine different concepts in more than a dozen countries on five continents and, while each study and each people is distinct, we identify several common themes. Firstly, traditional knowledge, values, and practices are central to the relationship between ITP and forest landscapes and underlie the effectiveness of other interventions. Secondly, early efforts to use forests to promote economic development have given way to a deeper understanding of the ways in which diverse products, services, and cultural values of forest landscapes support livelihoods for both ITP and other populations. Thirdly, governance is a common theme in this Special Issue, especially in terms of relations between ITP and the State and in the effectiveness of policies and programs. A fourth theme is that of understanding how the knowledge, practices and values of individuals and groups can help predict perceptions of forests and preferences for management. Finally, this Special Issue showcases a wide variety of methodological approaches, both qualitative and quantitative, helping researchers recognize the advantages and limits of each. Taken together, the papers in this Special Issue illustrate multiple characteristics of relationships between ITP and forest landscapes, and their aspirations to maintain their culture, their knowledge, their rights, and their livelihoods.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".