Assessing Public Participation for the United Arab Emirates E-Government
Bibliographic record
Abstract
Geographic and geospatial information processing represent vital human functions for locating phenomena and navigating in the world around us. Spatial representations as mental and physical maps tie perception to cognitive processes. As a system, digital information and communication technologies (ICT) connect satellites to global positioning systems (GPS) to allow accurate, real-time simulations and representations of space–time locations. Now embedded in a globally linked network of digital devices, these systems offer interactive space–time communication of qualitative (visual) and quantitative (locational coordinates) information. Our global information societies, digital economies, social media, governance, commerce, and industry exist because of these processes and are dependent on them. With potential for active user participation in GIS embedded in billions of digital devices, the primary questions of public participation GIS (PPGIS) investigate the means by which government, commerce, and civil society decentralize control and collectively agree on degrees of access, verification, encoding, storage, privacy, and proprietary uses of geospatial information. This research investigates such questions in relation to global cultural, social, and political differences among providers and users—government services, commercial, legal, and social-technical management. Our focus is on the United Arab Emirates, its unique geopolitical and economic position among global networks, and its internal development, demographic, and security issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".