Bibliographic record
Abstract
How did the idea that crime and development are connected become a global governance problem? Jarrett Blaustein, Tom Chodor and Nathan Pino trace the genealogy of this crime–development nexus from the nineteenth century to the present, focusing on the institutionalization of the global crime agenda at the United Nations. They show that the crime–development nexus ‘cannot be reduced to the empirical realities of the issue linkage alone because the relationship between these phenomena is complex and historically and contextually contingent’ (p. 3). Both crime and development are contested concepts, and establishing a clear relation between them is an inherently fraught undertaking. Adopting a neo-Gramscian approach based on the work of Robert Cox, Unraveling the crime-development nexus grounds ‘a constructivist analysis in institutionalist and materialist perspectives’ (p. 8). It stresses the role that international organizations play in the (re)production of hegemony, as they facilitate the spread of global capitalism and absorb counter-hegemonic challenges. As the authors argue, the history of the crime–development nexus cannot be understood outside the history of global capitalist development.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".