The critical impasse of peacebuilding: Toward an analytically eclectic critique of liberal peacebuilding
Bibliographic record
Abstract
In the decades following the Cold War, scholars of International Relations (IR) have struggled to come to grips with how the fundamental shifts in the international system affect the theoretical underpinnings of IR. The debates on peacebuilding have served as a fierce battleground between the dominant IR research programs—realism and liberalism—as to which provides both the best framework for understanding contemporary security challenges as well as policy prescriptions. I engage with the recent arguments made by David Chandler and Mark Sedra, two prominent critical scholars of IR, and argue that IR as a field would be best served to leave behind the “great debates” of the different research programs, and instead focus on middle-range problem-solving and analytically eclectic approaches. This essay further argues that the best way forward is for critical theorists to take a conciliatory approach with the contributions from the other research programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".