A Case Study of Counter Violent Extremism (CVE) Programming: Lessons from OTI’s Kenya Transition Initiative
Bibliographic record
Abstract
Between 2011 and 2014 the USAID Office of Transition Initiatives (OTI)’s Kenya Transition Initiative implemented what was essentially a pilot program of the new Countering Violent Extremism (CVE) concept. Aiming to counter the drivers of ‘violent extremism’ (VE), this operated through a system of small grants funding activities such as livelihood training, cultural events, community debates on sensitive topics, counselling for post-traumatic stress disorder, and so on. This paper delivers lessons from the program, generated via an independent evaluation, offering insights of relevance to the broader CVE community of practitioners. A first overarching conclusion is that programming decisions would have benefitted from a more comprehensive understanding of VE in the local context. For instance, subsets of the population more narrowly ‘at-risk’ of being attracted to VE should have been identified and targeted (e.g. potentially teenagers, ex-convicts, members of specific clans, and so on), and a greater focus should have been placed upon comprehending the relevance of material incentives, fear, status-seeking, adventure-seeking, and other such individual-level drivers. A second conclusion is that the KTI team would have profited from additional top-level guidance from their donors, for instance, providing direction on the extent to which efforts should have been targeted at those supportive of violence versus those directly involved in its creation, the risks associated with donor branding, and contexts in which the pejorative term ‘extremism’ should have been pragmatically replaced by neutral terminology. As a priority donors and the wider community should also provide suitable definitions of the CVE concept, rather than leaving practitioners to construe (undoubtedly inconsistently) it’s meaning from the available definitions of VE.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".