中国非政府组织的实践团体 (A Community of Practice for Chinese NGOs)
Bibliographic record
Abstract
The English version of this paper can be found at https://ssrn.com/abstract=2612686 Chinese Abstract: 实践团体是分享具体领域知识的重要资源。它是中国非政府组织互相学习合作的一个机制。根据100多个非政府组织的原始数据，本文考察了中国非政府组织培育一个成熟实践团体的组织能力。我们发现，如需实现这一目标，中国非政府组织必须克服固有的不利因素。另一方面，样本中的绝大多数非政府组织并不认为自身是专家团体的一份子，这对成立一个结构化、制度化的实践团体提出了巨大挑战。另一方面，为了在后慈善事业法制度下存续并发展壮大，中国非政府组织需要学习如何适应——即实施最佳做法并避免不良做法——从而实现存续。 English Abstract: A community of practice represents an important resource for the sharing of sector-specific knowledge. It is a mechanism for Chinese NGOs to learn from each other, and collaborate. Drawing upon original data elicited from over 100 NGOs, this article examines the organizational capacity for Chinese NGOs to cultivate a mature community of practice. We find that there are inherent headwinds that Chinese NGOs will have to navigate to accomplish this goal. On the one hand, the majority of NGOs in our sample do not see themselves as part of a community of experts, which presents a huge challenge for the possibility of a structured and institutionalized community of practice. On the other hand, in order to survive and prosper in the post-Charity Law regime, Chinese NGOs will have to learn how to adapt – implement best practices and avoid worst ones – to survive. This is best accomplished by developing a mature community of practice to share knowledge with each other.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".