Social justice and environmental integrity in biodiversity conflicts: searching for common ground and sustainability in the conservation of jaguar (Panthera onca)
Bibliographic record
Abstract
Abstract: Biodiversity conflicts occur when people's objectives or priorities over biodiversity differ. They are presented here as a symptom of our failures to reach sustainable development targets, sustainable development being defined in this study as a process aiming at environmental integrity and social justice. Previous research approached biodiversity conflicts primarily under the environmental integrity component of sustainable development, aiming to reduce conflicts through the reduction of biodiversity impacts (i.e., the negative interactions between humans and biodiversity). However, the implementation of strategies aiming to reduce biodiversity impacts has rarely led to long-term conflict management, suggesting that conflict management could be principally affected by social justice and the underlying human conflict. Through an interdisciplinary approach, I explore how the notion of social justice and the pursuit of common ground may help develop new solutions to manage biodiversity conflict and achieve better environmental integrity. More specifically, I try to understand: (1) What is the relationship between biodiversity impact and biodiversity conflict? (2) How is social justice related to biodiversity conflict, and how might its consideration offer new approaches or solutions to strengthen environmental integrity? (3) Can dialogic and collaborative approaches contribute to managing biodiversity conflicts? My research is based on an empirical study exploring environmental management in Calakmul, Mexico. Calakmul region, while hosting the largest tropical forest and population of jaguar (Panthera onca) in Mexico, is also a place for agricultural activity, resulting in a conflict about jaguar management. While jaguar management is a common thread among the chapters of my thesis, I also focus on the benefits of exploring multiple issues to understand the context in which environmental management takes place. In chapter 2, I assess the extent of large cats’ impact in the region and the factors that influence the occurrence of livestock attacks and their spatial distribution. I develop a two-dimensional approach to consider landscape characteristics and human pressure separately. I also use a geostatistical model, accounting for spatial autocorrelation in the data, as well as a multi-scale approach to select the relevant spatial scale for each variable and consider historical data on landscape attributes. Results show that sheep are particularly at risk, regardless of their spatial distribution in the region or other factors. Attack occurrence is best explained by the functional characteristics of the landscape (here, linked to fragmentation process), whereas the effect of human pressure is of lower importance. This research suggests that attack risk is widely spread across the Calakmul region, and that strengthening the use of landscape ecology for spatial predation risk estimation might improve the potential of such tool for conservation. In chapter 3, I propose a novel approach to start collaboration that…
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".