Linking "good" and "bad" civil society: how local networks promote peace or renewed violence in civil wars
Bibliographic record
Abstract
The notions of “good civil society” and “bad civil society” in early peacebuilding scholarship helpfully challenged prevailing essentialized notions of civil society. This study explored whether international aid to “good civil society” and the marginalization of “bad civil society” has actually helped promote peace in protracted conflicts, as this peacebuilding scholarship has asserted and is largely reflected in international aid practice. Choosing the ability to restrain “own-side violence” as one key measure of influence on peace, the study examines how international aid has impacted civic actors’ ability to restrain violence in protracted conflicts in Kosovo and Georgia. The cases compare pre-war periods marked by locally rooted forms of civic activism with post-war periods when international assistance supported the growth of a large local civil society along the lines of the “good civil society” normative model. The cases examine local civic dynamics on only one side of each conflict, to enable a detailed picture of the broad spectrum of civic actors present and probe the relationships amongst them. The study found little evidence that international support for local “good civil society” helped civic actors gain the legitimacy and influence with domestic constituencies needed to restrain own-side violence. Instead, in Georgia and Kosovo across these time periods, three other interlocking factors appeared to support the ability of civic groups to restrain violence. Applying a complex systems analytical lens illustrates how these multiple factors interacted with each other to support the influence and legitimacy of local civic actors. The study also showed that international certification of local civil society rarely was decisive for civic actors’ efficacy in restraining violence, but their credibility with their own ethnic constituency, or bonding social capital, was an indispensable asset.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".