Translating the Wild Boar in Brazil: The Challenges of Interessement in Actor-Network Theory
Bibliographic record
Abstract
The wild boar (Sus scrofa), an invasive alien species with high ecological plasticity, has become a global concern due to its environmental, economic, and social impacts. In Brazil, its uncontrolled expansion has triggered complex conflicts involving diverse stakeholders. This study explores the sociotechnical translations of the wild boar using the Actor-Network Theory (ANT), particularly Michel Callon’s Sociology of Translation, to examine how different actors construct and negotiate the species’ meaning and management. Through a qualitative case study approach, we identify how heterogeneous actors—rural producers, hunters, environmental agencies, scientists, and civil society—attribute competing values and roles to the wild boar. The findings reveal that the wild boar transcends its biological classification to become a hybrid actor shaped by dynamic interactions, conflicting interests, and political-ecological narratives. These translations are fluid, contingent, and influenced by power relations, media discourse, cultural perceptions, and institutional responses. The study contributes to a deeper understanding of human–non-human assemblages and the governance of invasive species, underscoring the need for interdisciplinary frameworks that account for environmental sustainability, socioecological complexity, and non-human agency in policy-making processes.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".