Analysis of sustainable non-agricultural livelihoods of urbanized farmers based on Structural Equation Model:A case study of Shuozhou city in northwestern Shanxi province
Bibliographic record
Abstract
Belonging to restricted development zones at both national and provincial level,northwestern Shanxi province has inherent vulnerability of natural ecosystems which have caused land desertification, water and soil loss, and farmers ?? poverty. Since 2000, the urbanization of farmers in ecological fragile region of northwestern Shanxi has been growing rapidly. Forming an interaction chain oftransformation from agricultural to non- agricultural livelihoods of urbanized farmers—farmland transfer—scale management of farmland—improvement of rural residents ?? income—ecological restoration and protectioncould provide a new way of reducing population pressure on land and restoring ecological environment in the study area. The key point of this interaction chain is the sustainable non-agricultural livelihoods of urbanized farmers. Using DFID model—a sustainable livelihoods(SL) framework which is the most widely applied all over the world as reference, and taking Shuozhou—a big city in northwestern Shanxi for an empirical study, this paper quantitatively measures the complicated influencing mechanism between vulnerability context, livelihoods capitals, livelihoods strategies,and livelihoods outcomes. The results show that:(1) the vulnerability context has a significant negative impact on livelihoods strategies, and it negatively influences livelihoods outcomes indirectly through livelihoods strategies as well.(2) Human capital, physical capital, and financial capital have a positive impact on livelihoods strategies of non- agricultural labor remaining in the city. In livelihoods capitals, only physical capital has impact on the nature of employment.(3) Human capital has a significant positive impact on both career level and richness of entertainment life. Social capital has not exerted significant impact on career level,but has positive impact on the income increase after the farmers moved into the cities.Financial capital shows a significant positive impact on the richness of entertainment life.(4)The livelihoods strategies of non-agricultural labor remaining in the city have a positive impact on the richness of entertainment life. Besides, the nature of employment displays a positive impact on income increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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".