New Trends in Peace and Conflict Impact Assessment (PCIA)
Bibliographic record
Abstract
Questions of effectiveness, impact and evaluation continue to be most relevant for the field of peacebuilding and conflict transformation: Adam Barbolet, Rachel Goldwyn, Hesta Groenewald & Andrew Sherriff report with intimate knowledge on the development of "conflict sensitivity" as an alternative to PCIA; Kenneth Bush sends thought-provoking "field notes", reflecting on his learning in the context of applying PCIA in the South; Thania Paffenholz presents a comprehensive overview of the "Aid for Peace Approach". Short reflection papers by all authors shed light on progress and controversy regarding the new trends in peace and conflict impact assessment.
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 | 0.160 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.027 | 0.036 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".