Indigenous Peoples, Representation and Citizenship in Guatemalan Forestry
Bibliographic record
Abstract
Forestry decision-making is still largely centralised in Guatemala. Nevertheless, elected municipal governments can now play a key role in local forest management. These local governments, with some exceptions, are the principal local institutions empowered to participate in natural resource authority. Some theorists argue that such elected local officials are the most likely to be representative and downwardly accountable. But do these political institutions have the ability to represent the interests of minority and historically excluded or oppressed groups? Latin American indigenous movements are fighting for new conceptions of democracy and practices of representation that recognise collective rights and respect for customary law and authority. How does this approach weigh against elected local government? This article compares how elected municipal governments versus traditional indigenous authorities represent the interests of indigenous communities in forest management. It traces the historical context of relations between indigenous people and the state in the region, and then presents the findings from case studies in two Guatemalan municipalities. The article finds that both authorities have some strengths as well as important weaknesses, thus supporting arguments for the reinvention of both liberal democracy and tradition in the interest of inclusive citizenship
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".