The Cost(s) of Geospatial Open Data
Bibliographic record
Abstract
Abstract The provision of open data by governments at all levels has rapidly increased over recent years. Given that one of the dominant motivations for the provision of open data is to generate ‘value’, both economic and civic, there are valid concerns over the costs incurred in this pursuit. Typically, costs of open data are framed as internal to the data providing government. Building on the strong history of GIScience research on data provision via spatial data infrastructures, this article considers both the direct and indirect costs of open data provision, framing four main areas of indirect costs: citizen participation challenges, uneven provision across geography and user types, subsidy of private sector activities, and the creation of inroads for corporate influence on government. These areas of indirect cost lead to the development of critical questions, including constituency, purpose, enablement, protection, and priorities. These questions are posed as a guide to governments that provide open data in addressing the indirect costs of open data.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".