SOCIO-ECOLOGICAL THEORIES AND EMPIRICAL RESEARCH. COMPARING SOCIAL-ECOLOGICAL SCHOOLS OF THOUGHTS IN ACTION
Bibliographic record
Abstract
Environmental problems, at local scale as well as global scale, are now considered as key issues and scientists are encouraged to be part of the process to address these issues. For the last decades, scholars have been focusing on the study of interactions between social dynamics and ecological processes and produced a set of concepts and scientific discourses aiming at framing the analysis of socio-ecological dynamics and eventually at orienting interventions. Scientific discourses are produced by scholars who belong to different groups (resilience, vulnerability, political ecology, commons, robustness ¿) which identity go beyond disciplines, methods, frameworks and concepts and include a collective history on the evolution of ideas and research organization. Different research units based in Montpellier (France) have been conducting research on socio-ecological systems (SES) to understand many types of relationships, including those between agriculture and biodiversity, policies and landscapes dynamics, watershed management, ecosystem management and health risk. A project named SETER (Socio-Ecological Theories and Empirical Research) was elaborated aiming at assessing the relevance and the complementarities of theoretical frameworks by applying and testing them on several empirical research case studies developed by the participating research units based in Montpellier. We have confronted different scholars, holding the flags of different schools of thought, to the same concrete issues. The purpose of this assessment was to clarify the respective potential of the different theoretical frameworks and to provide the basis for new conceptualizations of socio-ecological systems dynamics and management. Although some authors call for an integrated framework and some advocate for the diversity of explanations, both agree on the fact that there is a need for clarification for a better scientific debate and better interactions with the managers, stakeholders and interested people. The scientific debate remains obscure when it is based on abstract developments. This report presents the material of this experiment, the interaction process and the lessons on the schools of thought and methodologies. Firstly, we introduce briefly the different schools based on the literature and on the presentations given by the invited scholars. Then we introduce the four case studies which were analyzed by the invited scholars and report on the interactions and their results. Then we present a discussion proposing a classification of the different perception of change which crosses the different schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".