Organization of Public Safety Networks: Spillovers, Interoperability, and Participation
Bibliographic record
Abstract
We analyze trade‐offs in the organization of public safety networks when network assets are distributed across districts and a district values network assets in its own and other districts. Comparing centralized, decentralized, and mixed organization forms, we capture two critical properties: interoperability among distributed technology‐based network assets and the ability of districts to opt‐in or opt‐out of the centralized form. We model the provision of public safety networks, where network assets are chosen by each district or by a federal government, where these assets have a positive cross‐district spillover that depends on interoperability, where investments in effort can be made to improve interoperability, and where districts can opt‐in or opt‐out of centralized provision. With the adoption of centralized, decentralized, or mixed provision as a result of districts' opt‐in or opt‐out choices, we identify conditions that determine when the districts deviate from the social optimum and thus regulatory intervention is beneficial to incent the socially optimal organization form. We show how the socially optimal organization form can be achieved through policy instruments such as a sharing rule for the cost of interoperability effort and direct government grants.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".