Re-theorising the participation-security nexus in war-to-peace transitions
Bibliographic record
Abstract
War-to-peace transitions feature multiple insecurities not directly connected to armed conflict, seen clearly in violence associated with social and political participation by marginalised persons and communities. Understanding such violence requires re-theorisation of the relationship between citizen participation and security. Grounded theory research on sectoral and temporal patterns of violence in Colombia’s Cesar Department suggests it is utilised by societal elites in response to grassroots participation. I analyse electoral participation, intra- and inter-community participation, and participation in dialogue with authorities at multiple levels, finding that empowered citizen participation is a key means to ensure citizen security. This includes participatory peacebuilding mechanisms during 2011–2016 peace negotiations with FARC-EP, involvement in ongoing peace accord implementation, and elaboration of local development plans. Re-theorising the participation-security nexus challenges ‘security first’ analyses and calls on academics, practitioners, and policymakers to consider the motives, methods, and mechanisms through which violence is utilised to block empowered citizen participation.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".