THE MOBILITY OF POLICE-CITIZEN INTERACTIONS OPEN DATA
Bibliographic record
Abstract
In 2014, as protests against police brutality spread throughout the United States, a new policy began to emerge that sought to address the public’s declining trust in law enforcement through the release of previously withheld information on daily interactions between police and citizens. As part of a larger movement promoting government transparency, often called open data, this novel application of open data to policing was a dramatic change compared to the status quo concerning police data on these interactions in the United States. This dissertation examines the genesis, development, and spread of this policy, referred to as police-citizen interactions open data (PCI open data), focusing on the role played by the White House-led Police Data Initiative (PDI). This is achieved through developing an integrated analytical framework that combines insights from the assemblage/mobility approach with institutional perspectives on police agencies, which is then applied on original qualitative and quantitative data. This dissertation emphasizes the importance of informational infrastructure assemblages, such as the PDI, in facilitating policy mobility, and presents evidence of PCI open data adoption and mutation among data transparency policies utilized by American police.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".