Development and Conflict Prevention: Reflections on Rwanda - An Interview with Professor Peter Uvin
Bibliographic record
Abstract
Development assistance organizations are paying greater attention to the causes of conflict, asserting a link between poverty and violence and affirming the potential of designing more conflict prevention sensitive development policies and programs. The Liu Centre for the Study of Global Issues at the University of British Columbia and the Canadian International Development Agency recently hosted the Vancouver Roundtable on Development, Conflict and Peace building: Responding to the Challenge, on February 14-15, 2002, where invited experts explored the possibilities of linking development and conflict prevention more effectively. Noted for his extensive field experience in following up the OECD-DAC Guidelines on Conflict, Peace and Development in 1999, as well as recent articles in 2001 and 2002 and a book in 1998 on the role of the international development agencies in Rwanda, invited Roundtable participant Professor Peter Uvin of Tufts University agreed to be interviewed by IIR Liu Centre post-doctoral fellow Erin Baines to elaborate his framework for taking action, presented at the Vancouver Roundtable. Given that each conflict situation is different, too unique, too idiosyncratic to generalize across cases, as you wrote in Reflections of Good Donor Practice, can we expect from a Canadian strategic response to the issue of development and conflict prevention? There are seven features that characterize a good policy on development and conflict prevention. These seven features are relevant for non-conflict prevention related development work as well. They hold for all development work, suggesting that there is nothing unique about conflict prevention work. Now it would be interesting to tease out would be specific only to development trying to prevent conflict, as opposed to development just doing development. But do such situations exist? I do not believe you can come to very meaningful and strong predictors about violent conflict or you can do to prevent violent conflict. All you can do is ask the right questions and set up mechanisms and lenses that allow you to constantly be aware and adapt. Some of these seven features are widely accepted, while some are widely non-accepted, or non-practiced. First, you have to be prevention-oriented as a matter of total routine; it ought to be so ordinary that you do not need to talk about it any more. For example, it is now expected that, if you were to do anything in development and you totally disregard gender issues or poverty issues, people will ask you ... what decade are you coming from? This ought to be the case with conflict. This is both a relatively easy one, for it requires little money and time, and a very hard one, for it is at this level that much resistance exists. Second, you have to be knowledge based; you will never be able to deal intelligently with conflict dynamics without good knowledge. I think that is widely accepted. The question is whose knowledge counts, which is a very important and totally undervalued matter. We have to put vastly more resources into local knowledge generation. But I think that is the case also for non-conflict development work. You have to always invest in local knowledge creation to ensure that there are strong and existing dynamics for intellectual reflection and speaking out about conflict issues once they force themselves onto the agenda. This is one thing that foreign aid can do better than others, to create the kind of spaces for people to do this, because in [cases of violence] these spaces often do not exist or are weak. So that is something that could be done better.... Third, we ought to be more flexible in financial mechanisms and administrative mechanisms, capable of making short-term change with a long-term perspective. We typically do not have much of a long-term perspective. At the same time, we aren't very good at the short term either, wherein overnight or in the course of a week we can dramatically change course. …
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".