Distributed spatial data sharing: a new model for data ownership and access control
Bibliographic record
Abstract
With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".