Models of direct editing of government spatial data: challenges and constraints to the acceptance of contributed data
Bibliographic record
Abstract
The current popularity of government open data platforms as a way to share geospatial data has created an opportunity for government to receive direct feedback and edits on this very same data. This research proposes four models that can define how government accepts direct edits and feedback on geospatial data. The four models are a “status quo” of open data provision, data curation, data mirroring, and crowdsourcing. These models are placed on a continuum of government control ranging from high levels of control over data creation to a low level of control. Each model is discussed, with relevant challenges highlighted. These four models present an initial suite of options for governments looking to accept direct edits from data end users and can be framed as a partial realization of many of the principles of open government. Despite the varied potential of these approaches, they generate a shift in locus of control away from government, creating several areas of risk for government. Of these models, near-term interest may focus on data curation and data mirroring as evolutionary, rather than revolutionary steps that expand on the simple provision 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 | 0.046 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".