Provisioning Interoperable Disaster Management Systems: Integrated, Unified, and Federated Approaches
Bibliographic record
Abstract
In this paper, we analyze the choice of interoperability approach for the provision of disaster management systems (DMS) when resources are distributed across districts, and in times of disaster resources can be shared. The degree to which sharing (a spillover) can be coordinated efficiently depends on resource interoperability. In this public sector setting, we model the provisioning of DMS as the choice between interoperability approaches; in decreasing order of centralization they are integrated, unified, and federated. A unique feature of our setting is that the interoperability approach is a collective decision by districts. Districts choose their own DMS resources and interoperability effort, and face different interoperability efficiency and technology misfit costs depending on the interoperability approach. We find that any approach can be an equilibrium depending on interoperability efficiency, and that when the social optimum deviates from the equilibrium the socially optimal approach is more centralized. When subsidies and taxes are implemented, the socially optimal interoperability approach can be achieved with budget balance. When only subsidies can be used, the socially optimal approach can be achieved but only under certain interoperability efficiency and misfit cost conditions is there a net social gain. Having an initial level of interoperability causes the equilibrium interoperability approach to shift toward a less centralized one. Our results generalize to other settings characterized by interoperability concerns, collective decisions, and spillovers.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".