GeoDF: Towards a SDI-based PPGIS Application for E-Governance
Bibliographic record
Abstract
Research and development of Public Participation Geographic Information Systems (PPGIS) has been a branch of GIS study for more than a decade. Using WebGIS and communication tools for public participation, both citizens and municipalities benefit from a more efficient "24/7", GIS-enabled communication and information-sharing platform. PPGIS demands open access to information, and the success of such applications relies heavily upon the availability of appropriate geospatial information. The framework data and institutional mechanisms offered by (particularly local) Spatial Data Infrastructures (SDIs) have the potential to offer an open and ideal environment for PPGIS applications. This paper investigates the potential integration of PPGIS into existing SDIs to empower grassroots communities, increase citizen participation and enlarge the use of geospatial information by the general public. Building on earlier PPGIS research conducted at UNB, a GIS-enabled online discussion forum (GeoDF) prototype is now being implemented as a pilot project in
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".