Perspectives on socio-ecological studies in the Northern and Southern Hemispheres
Bibliographic record
Abstract
Abstract Socio-ecology studies the relationships between human activities and natural systems and their importance in management and public policy. Our objective was to analyse how published papers in countries with a high Human Development Index (HDI) perform socio-ecological studies and compare them between the Northern and Southern Hemispheres. To do this, we used the Scopus platform as a source for searching and obtaining scientific papers about socio-ecological studies conducted in countries from the Northern and Southern Hemispheres. We calculated the number (n) of papers published per year and classified them using the main subject areas of the SCImago Journal & Country Rank database. Then, we analysed whether papers included specific recommendations for natural system management, nature conservation, policies or governance structures, or science in general. Besides, we studied whether the papers addressed socio-ecological studies related to flora and fauna and from what specific group of organisms or systems. Data were compared using the chi-square (χ2) test (Pearson p < 0.005). A total of 467 papers were analysed, where 34% were from the Southern Hemisphere (mainly Argentina, Australia, Chile, and South Africa) and 66% from the Northern Hemisphere (mainly the USA, Canada, and Spain). The Northern Hemisphere (mainly North America and Europe) played a major role in the socio-ecological knowledge exchange than the Southern Hemisphere (South America and Africa). The results showed socio-ecological studies focused mainly on generating management recommendations in social and environmental science fields. The number of studies coming from the Northern Hemisphere was significantly higher than those from the Southern Hemisphere. Most of them were conducted at a local level (e.g., watersheds or human settlements) in three different systems (i) terrestrial (e.g., forests or grasslands), (ii) freshwater (e.g., rivers or streams) and (iii) marine (e.g., coastlines or seas). Most of the studies (70%) were conducted in production systems, where the majority included livestock (mainly bovine) and aquatic fisheries (e.g., salmon, artisanal coastal fishing, or trout). Most vegetation papers (65%) were on native forests. Papers on wildlife made up 30% of all animal-related studies, with mammals, birds, and marine invertebrates (such as collars) being the most extensively researched species. This work highlighted the socio-ecological approach that was used in the analysed countries with greater HDI to develop management options for natural systems.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".