Expertise and policy influence during international transition: Astri Suhrke confronts post-conflict peace and development in the 1990s
Bibliographic record
Abstract
This article reports three examples among Astri Suhrke’s manifold efforts to use her research and reasoning to influence intervening powers and organisations in post-conflict peace and development: explaining the concept of human security in the 1990s normative contest to redefine international humanitarian and peacebuilding interventions in a post-Cold War international system, warning the Tokyo donors’ conference for Afghanistan against undermining the political agenda with their economic agenda, and demonstrating the serious statistical flaws in Paul Collier’s advice on post-conflict aid and conflict recurrence after civil wars. It reflects on reasons why these efforts were unsuccessful, including the power of the World Bank, practitioners’ romance with statistics, the continuing balance of international power in favour of the United States and within that context, Canada’s attention to its role on the Security Council during the Kosovo conflict and NATO bombing, and the victory instead of the doctrine of Responsibility to Protect and the concept of failed states. It concludes that far more lasting than these momentary battles are Astri’s vast research, moral commitments, and creation and nurturing of an entire generation of younger scholars.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".