Bibliographic record
Abstract
Public participation is widely regarded as a vital component for making environmental decisions more democratic, legitimate, and effective. Yet, research on this subject has largely focused on rights and principles instead of context and process, especially in non-Western settings. To address this gap, this article explores how local voices on environmental issues were muted in a Chinese rural context. It describes controversies surrounding a cultural and ecological tourism development in Heyang, a transforming village in the east coastal region of China. Based on semistructured group interviews, the article reveals that although many issues found in the Heyang case resonated with similar cases in Western settings, such as the lack of access to information and the problematic solicitation of public input, fundamentally, the local voices were muted by the village council’s blind adoption of an urban-centric ecological modernization agenda and its neglect of local villagers’ emotional attachment to their land properties. The above findings not only draw our attention to how participatory communication can be compromised by contextual factors but also invite us to reconsider how China’s existing urban–rural division fundamentally influences its ecological civilization.
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 | 0.033 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".