专制主义环境下的NGO 策略，及对公民身份的影响：中国案例 (NGO Strategies in an Authoritarian Context, and their Implications for Citizenship: The Case of the People's Republic of China)
Bibliographic record
Abstract
The English version of this paper can be found at： http://ssrn.com/abstract=2657187. Chinese Abstract: 本文认为在中国不同的城市里，NGO 所处的资源环境也是不一样的。为应对不 同的资源环境，NGO 需要因地制宜制定合适的资源策略，这又反过来塑造了NGO 的特征和结构。本文还进一步探讨了这些特征和结构是如何影响专制主义环境公下民身份的构建和表现。特别是,某些类型的NGO 鼓励人们采取消极态度,而其他NGO 则为公民提供了主动参与社会事务的方式。通过对中国四个城市(北京、上海、昆明和南京)的NGO 进行分析,揭示了三种不同类型资源环境和行为模型，本文的研究结果很好地证明了这个观点。本文也讨论了每种模型对于公民参与的影响。 English Abstract: This study argues that different cities in China have different resource environments available for NGOs. Organizations react to these resource environments by constructing appropriate resource strategies, which in turn shape the characteristics and structures of the NGOs of that city. It further examines how these characteristics and structures influence the construction and performance of citizenship in an authoritarian environment. Specifically, some types of NGOs encourage Chinese citizens to be passive, while others offer a model for people to actively engage with social issues. This is aptly demonstrated in an analysis of NGOs operating across four cities – Beijing, Shanghai, Kunming, and Nanjing – which reveals three different types of resource environments and behavioural models for NGOs. We subsequently discuss the implications of each model for citizen engagement.
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 | 0.003 | 0.002 |
| 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 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".