A grounded theory of local ownership as meta-conflict in Afghanistan
Bibliographic record
Abstract
Internationally sponsored interventions in fragile and conflict-affected states are often resisted by domestic actors who have deep local knowledge, profoundly different expectations of political processes, and keen desires to shape their country’s future. Many forms of local resistance can damage or stall the progress of externally driven peacebuilding, but the critical peacebuilding literature has suffered from an inability to articulate coherent strategic alternatives to the dominant paradigm of liberal interventionism. This paradigm, we argue, is actually part of what fuels continued resistance: as external actors seek to implant liberal democratic norms into local bureaucratic and political cultures, countless sites of conflict emerge, with local and international actors jockeying between and amongst each other for position, resources, and control over the specificities of reform. These struggles – effectively a competition over local ownership – are at the centre of peacebuilding and will determine short- and long-term intervention outcomes. Focusing on the case of political reform in Afghanistan, this article develops a grounded theory of ownership as ‘meta-conflict’, in which participant voices from local and international peacebuilding leaders, working in-country, are given a primary role in determining the compatibility of the donor community’s prevailing liberal agenda with local requirements for building peace.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 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".