Informality as an approach to claiming the right to resettlement and achieving inclusive rural-to-urban resettlement for landless villagers: The case of Hangzhou, China
Bibliographic record
Abstract
This article discusses how to achieve inclusive resettlement for landless villagers amid China’s promotion of urbanization through resettlement. This research conceptualizes the right to resettlement in China by synthesizing the literature on resettlement, the right to the city, and informality. This research captures four subsets of rights to resettlement based on a review of existing resettlement literature, including rights to economic enhancement, spatial adaptation, social stability, and political inclusiveness. While state-led resettlement policies should have prioritized inclusive resettlement, our case study reveals the significant role played by villagers’ bottom-up approaches, utilizing informality and collective strategies, in enhancing inclusiveness. The research adopts an explanatory-sequential approach that uses principal component analysis, semi-structured interviews, and questionnaire surveys to investigate post-resettlement adaptation in 12 resettlement communities in Hangzhou, China. The empirical evidence suggests informal economic activities, spontaneous spatial transformation, hybrid governance structures, and non-institutionalized participation have contributed significantly to villagers claiming their right to resettlement. We conclude with recommendations for achieving inclusive resettlement.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".