Participation and transdisciplinarity in Ecohealth: a social network analysis perspective
Bibliographic record
Abstract
Abstract Ecohealth uses participatory and transdisciplinary approaches to understand the relationships between the components of socio-ecological systems and how these interactions influence the health of human populations. This article aims to use Social Network Analysis (SNA) to understand the role of collaborative relationships between the various actors involved in participatory and transdisciplinary processes in Ecohealth projects. We present a set of SNA indicators to characterize the evolution and equity of participation and to differentiate inter- and transdisciplinarity. The analysis was based on the collaboration network among the members of the Iniciativa Para el Liderazgo y Desarrollo del Campo de Ecosalud y Enfermedades Transmitidas por Vectores (ETV) en América Latina y el Caribe. The participatory process intensified throughout the project, with more individuals involved and increasing collaborations. Cooperation between members from social, environmental, and health sciences is unbalanced and health scientists predominate. The few environmental scientists are, however, actively involved in interdisciplinary collaborations. The proposed approach has wide application to study participation and transdisciplinarity in projects about health and environment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".