Pre-decisional Engagement, Decision-Making Outcomes and Web-PPGIS Usability
Bibliographic record
Abstract
This paper explores the relationships among the usability of a Web-based Public Participatory GIS (Web-PPGIS), the degree of pre-decisional engagement and decision outcomes in the context of a real-world participatory planning project. ArgooMap is used to support local residents in an online procedure for determining the “optimal” location of a new parkade in Canmore, Alberta. UsaProxy is employed to automatically collect the datasets on the system usability and the degree of predecisional engagement. This research shows that the degree of pre-decisional engagement depends significantly on the system usability measured in terms of the system efficiency, effectiveness and the user satisfaction with using the system. In addition, the degree of pre-decisional engagement has a significant effect on the decision outcomes (i.e., the rankings of candidate sites). These findings provide clues for advancing Web-PPGIS.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".