The stages of transfer: Explaining the divergent forms of zero-tolerance policing in Oakland, California and Lima, Peru
Bibliographic record
Abstract
Social scientists have long been interested in understanding how policies transfer from place to place. Most of the studies focused on this question have investigated the mechanisms involved in transfer and the factors influencing adoption of certain policy “innovations” (often described as “policy learning”). Recently, however, critical policy transfer scholarship has raised key questions about how and why policies transfer, what policies look like once implemented, and how to effectively identify the local factors shaping distinct implementation of the same policy. The present study sheds light on these issues by conducting sequential ethnographic comparison of zero-tolerance policing of local forms of “disorder” in two locations—East Oakland, California and the district of La Victoria in Lima, Peru. To trace the development of zero-tolerance policing and identify key junctures and factors that drove divergent implementations between these cases, it employs a novel “stages of transfer” approach that disaggregates the policy transfer process into three discreet stages: (1) rationale for adoption , (2) the interpretive framework of authorities , and (3) implementation by street-level bureaucrats. Applying this approach, it shows how Oakland and Lima went from initial similarity to increasing divergence as this policy was translated by officials and street-level bureaucrats. The study presents a systematic model for policy mobilities research and contributes to criminological debates on the spread of US-style crime-control policy to different parts of the world, revealing the ways these penal practices are (and are not) making their way abroad.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".