Telecoupled Sustainable Livelihoods in an Era of Rural–Urban Dynamics: The Case of China
Bibliographic record
Abstract
Recently, increasingly sophisticated studies have investigated the relationship between agrarian livelihoods and the environment, as well as rural–urban interactions in developing countries. The policies developed to respond to these dynamics can constrain livelihood options or provide additional opportunities. In the present study, using a modified version of the telecoupled sustainable livelihood framework to generalize dynamic livelihood strategies in the context of rural–urban transformation and by focusing on recent research in China, we review important factors that shape rural livelihood strategies as well as the types of strategies that typically intersect with livelihood and environmental dynamics. We then examine telecoupled rural–urban linkages given that the dynamics of the livelihood strategies of farmers can cause flows of labor, capital, ecosystem services, and other processes between rural and urban areas, thereby placing livelihood strategies in a dynamic context, which has not been considered widely in previous research. We show that most previous studies focused on the reduction of environmental impacts via livelihood diversification and rural–urban migration. We propose several areas for future policy development and research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".