Developing geographic information infrastructures for local government: the role of trust
Bibliographic record
Abstract
The United States's National Spatial Data Infrastructure (NSDI) model presumes that the local government agencies of counties and municipalities will share their geographic information freely with government agencies of regions, states and federal agencies. This article takes up the issue of local government involvement in the NSDI by asking the question: why should local governments involve themselves in the NSDI? This question is informed by considering the social and technological imbrication of the NSDI . One of the oldest spatial data infrastructure projects, the NSDI offers insights into the complexity of implementing infrastructure in federal models of shared governance. This article focuses on the political and financial dimensions of developing infrastructure among local governments. Trust is quintessential at this level of government. Local government agency activities experience an inherently closer coupling with political representatives and with different agencies in both intramunicipal and intermunicipal activities. Building the NSDI is fundamentally an interagency act and thus a matter of trust. Trust is a key issue in the development of the NSDI, as the results of a study of Kentucky local government agencies indicate .
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".