Coproducing spatial information: Exploring government approaches and motivations at the local level
Bibliographic record
Abstract
Recent government initiatives like e-government and open government have led to broader adoption of geospatial tools including mapping platforms to access, use, and analyze open data. These advancements open channels for coproduction in the form of sharing information, change notifications, opinions, or requests to government, based on citizen observation and local knowledge. Though current government initiatives have substantial potentials for coproduction, the practical adoption and implementation of such practices vary reflecting the purposes, contexts, and motivations of those involved. This paper aims to understand how local governments are following different approaches to coproduce information with citizens and what motivates local governments in this process. We report findings based on interviews with 11 cities from the USA and Canada, which reveal four main approaches: the collection of new data, observation of changes, collection of opinions, and observation of preferences involving both explicit and implicit processes. Although these four approaches result from interactions between citizens and government, our findings also indicate a key role to be played by technology and partner organizations.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".