同构压力，认知群体，政府-NGO 合作在中国 (Isomorphic Pressures, Epistemic Communities and State-NGO Collaboration in China)
Bibliographic record
Abstract
The English version of this paper can be found at http://ssrn.com/abstract=2319035. Chinese Abstract: 该论文指出，中国政府与非政府组织（NGO）之间合作的缺乏并不能完 全归咎于政府对该领域发展的限制，或是出于对一个潜在的政府反对者的害怕。与北京和上海的NGO 访谈显示， 政府与NGO 之间缺乏有意义的合作的部分原 因是同构压力，以及政府对于NGO 活动认知的缺乏。事实上，证据显示，一旦政府获得了对NGO 工作的认知，它将会更加愿意与NGO 建立联系。当然必须说明的是，政府想要利用的是NGO 的物质资源，而非他们的象征性，阐释性，或是地理上的资本。 English Abstract: This article suggests that a lack of meaningful collaboration between the state and NGOs in China is not solely a result of the state seeking to restrict the development of the sector, or fear of a potential opposing actor to the state. Instead, interviews with NGOs in Beijing and Shanghai suggests that a lack of meaningful engagement between the state and NGOs can be partially attributed to isomorphic pressures within state-NGO relations, and insufficient epistemic awareness of NGO activities on the part of the state. In fact, the evidence suggests that once epistemic awareness is achieved by the state, they will have a stronger desire to interact with NGOs – with the caveat that the state will seek to utilize the material power of NGOs, rather than their symbolic, interpretive or geographical capital.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".