Trust in civil wars : the implications of conflict character and threat on political and social trust
Bibliographic record
Abstract
My research investigates the repercussions of protracted civil wars on bystanders’ political and social trust. The literature is fraught with inconsistent findings on how violence impacts trust. I argue that civil wars have distinct effects on trust primarily because wartime trust formations vary by the character of the conflict (ethnic vs. ideological) and the macro historical dynamics of the country, which together shape collective threat framing. Ethnic wars should induce a higher political trust for politically represented ethnic group via the state discourse’s emphasis on collective threat, even in the presence of personal threat. In ideological wars, a similar discourse on collective threat forwarded by the state is less likely, and in the absence of a higher national threat framing, personal insecurities extending from the war should diminish people’s trust in governing political institutions. Regarding social trust, ethnic violence renders in- and out-group distinctions visible and decreases out-group trust. Alternatively, ideological violence diminishes general trust (trust in unknown others). I deploy mixed-methods, combining case studies and cross-national quantitative data analysis. The two cases are the territorial Kurdish insurgency in Turkey (1984-) and the Maoist insurgency in Peru (1980-1992). I spent six months in each country and conducted archival work, comparative historical analysis, and numerous interviews and focus groups in 2013–2014. To see whether the theoretical predictions and empirical findings from Turkey and Peru can travel beyond their boundaries, I analyzed a pooled time-series cross sectional dataset (1981-2015), using multi-level models. As well as being one of the first qualitative studies of trust in conflict settings, my work is also original in distinguishing between the effects of different types of civil wars on trust, disentangling the impact of collective and personal threat, and showing that the effects vary in the society along ethnic and political lines. My empirical findings also shed light on the generation of collective threat framing using a macro historical lens, and suggest that state-building conditions both the nature of the insurgency, national and ethnic identities, and how the conflict will be framed by the state via the official discourse.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".