Bibliographic record
Abstract
In the face of the repeated failure of international peacebuilding to build peace, one strand of the literature argues that failure can only be understood by ‘zooming in’ – by focusing on peacebuilders, the local populations they purport to help, and the relationship between them. This article draws on the insights of this literature to argue that international peacebuilding should be understood as an instance of structural injustice. Studies of the encounter between international interveners and local populations tend to focus on the differences between these groups and their problematic relationship. I argue that ‘zooming in’ reveals much more than the differences between interveners and locals: it uncovers how their relationship presents parallels and similarities with others, such as the relation between colonizers and colonized. The relationship between internationals and locals is problematic not because of each group’s characteristics and their difference, but because of the social positions they relate from. These hierarchical social positions give some groups the power to intervene in the lives of others. The article argues that the encounter between internationals and locals should be ‘de-exoticized’ and that hierarchy, rather than difference, should be at the centre of the critical peacebuilding literature.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".