What Is the Role of Public History and Environmental Oral History in Supporting Conservation through Agroecology?
Bibliographic record
Abstract
Indigenous peoples and local communities are key actors in the preservation of important biodiversity resources around the world. However, the ever-encroaching agricultural frontier and expansion of conventional agricultural practices threaten these communities, their autonomy over the land, and the traditional knowledge and practices associated with biodiverse ecosystems. Agroecology emerges as an important solution to support the continuation of agrobiodiversity, food security, and environmental conservation, but top-down solutions often do not resonate with the lived realities of traditional, Indigenous, and small-scale farming communities. This paper examines a collaborative research and narrative network developed over the past several years around traditional erva-mate agroforestry production in Southern Paraná, Brazil. It offers an example of how oral environmental history and public history can support conservation practices through agroecology. The key outcomes of this interdisciplinary, multi-dimensional research and engagement were the development of a candidacy for the system to be recognized as a Globally Important Agricultural Heritage System (GIAHS) from the Food and Agricultural Organization of the United Nations (FAO) and the implementation of a Dynamic Conservation Action Plan to address the threats and challenges farmers and communities are facing. The discussion explores two concepts that were integral to these processes, the creation of narrative networks and a focus on plurivocity. Both approaches ensured that the actions, knowledge, and narratives developed through the GIAHS candidacy were not imposed but agreed upon and generative through narrative and dialogue, remaining true to the realities and lived experiences of community members.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".