Bibliographic record
Abstract
Democratic policing is a multidimensional, multilevel, and contested concept rooted in political ideology. It is not singular or politically neutral. I argue there are four typologies of democratic policing: right, centre-right, centre-left, and left. In Latin America, in the 1980s and 1990s, countries went through the dual processes of democratisation and the implementation of neoliberal economic policies. The latter increased inequality in wealth and led to deeply divisive debates regarding the place of equality and violence in the definition of democracy. Putting aside these debates on the meaning of democracy, police reform projects in Latin America have embraced community-oriented policing as synonymous with democratic policing. Yet, democratic policing is not a singular concept and political debates matter to its various meanings. The article uses Goertz’s (2006. Social science concepts: a user’s guide. Princeton University Press) three-level concept analysis to assess the theoretical similarities and differences between the four types of democratic policing. It then tests the theory with empirical data from the cases studies of Argentina (Menem and Kirchners) and Chile (Bachelet and Piñera). The case studies are informed by field research in both countries (2006–2015), and draw on media and human rights reports as well as secondary data. The study finds a gap between theory and practice that calls for more research on policy convergence. More importantly, it reveals the need to situate ideal definitions of democratic policing within political debates on democracy, paying close attention to the role of political ideology.
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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.078 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".