The role of social learning for social-ecological systems in Korean village groves restoration
Bibliographic record
Abstract
Recently, social learning has been recognized as a means to foster adaptation to changing conditions, and more broadly, social-ecological systems resilience. However, the discussion of social learning and social-ecological resilience in different cultural contexts is limited. In this study we introduce the Korean Village Groves Restoration Project (VGRP) through the lens of social learning, and discuss implications of the VGRP for resilience in villages impacted by industrialization and decline of traditional forest resources. We conducted open-ended interviews with VGRP leaders, government and NGO officials, and residents in four villages in South Korea, and found that villages responded to ecosystem change in ways that could be explained by the characteristics of social learning including interaction, integration, systems orientation, and reflection. However, the processes of learning varied among the four villages, and were associated with different levels of learning and different learning outcomes related to changes in village grove management and governance. The cultural and historical context can be used to help understand social learning processes and their outcomes in the Korean cases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".