Development engagement with organised crime: a necessary shift or further securitisation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractOrganised crime is increasingly being recognised as a development problem as well as a security threat. Underdevelopment creates a conducive environment for crime, while illicit flows undermine development progress. In response, development actors have begun to consider organised crime in their programming. However, there remains a reluctance to directly engage with organised crime, as there are fears that development will be further securitised. This has parallels with the reluctance of development actors to engage with conflict in the 1990s. This article draws on emerging reports from development agencies that begin to consider how organised crime can be addressed in development programming, comparing it to Goodhand's earlier framework on development and conflict. It evaluates what the different levels of engagement—working around crime, working in crime(-affected countries) and working on crime mean for the securitisation of development, assessing whether development engagement with organised crime is a necessary shift or further securitisation. AcknowledgementsAn earlier version of the article was presented at the ISA Conference in Toronto, March 2014. The author would like to thank the discussant and other participants for their comments.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it