Threading the Needle of Violence: Pursuing Overlapping Dynamics to Support Urban Peace
Bibliographic record
Abstract
In 2015, 193 United Nations member states adopted the 2030 Agenda for Sustainable Development and its 17 Goals (SDGs). The Agenda, although imperfect, opens important space to discuss, analyze and invest in the ways in which different aspects of social, political and economic life influence one another. The Agenda takes specific challenges—organized crime, climate change, gender inequality, etc.—and provides an integrated framework that is both universal (applicable to all countries) and inclusive (applicable to all people). Those working on organized crime would do well to better use the power of Agenda 2030 and the SDGs to advance balanced approaches that can both reduce violence associated with crime in the near term and the dynamics enabling organized criminal behavior in the long term.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".