Involving the public in spatial decision making using Internet GIS
Bibliographic record
Abstract
Public participation is an integral part of legislation or decision making processes. Traditionally, public participation took place through face-to-face encounters such as public meetings and other fora. However, some important factors limiting the efficiency and effectiveness of this mode of public participation include: geographic separation of participants, scheduling and financial constraints in attending meetings, and limited duration of meetings. These led to the awareness that public participation requires new methods in order to achieve a better democratic decision making. On the other hand, GIS has in the past been accused of being an elitist technology, giving more power to those people already possessing it and depriving those, namely the general public, who more often lack such direct forms of information access. Public participation GIS (PPGIS) is emerging as a distinct subset of two previously separate activities: technology-based spatial analysis and participatory democracy. The paper considers both traditional methods and Internet-based technologies of public participation and argues that new Internet-based technologies have the potential to widen participation by using online spatial decision support systems. GIS and the Internet can be used together to provide the general public with a powerful mechanism for becoming more involved in decision problems. Provision of full access to spatial and non-spatial data, along with the appropriate tools with which to use it, may greatly empower the general public. PPGIS focuses on engaging the public to participate and become involved in a particular subject of interest. It empowers GIS users from all walks of life and enabling them to use the technology purposefully to capture their local knowledge and advance their goals. In the project of public participatory Ontario nuclear waste siting, we focused on developing an Internet based PPGIS prototype to help the public to participate online from inception to the final phase of site decision-making. It shows that in certain siting problems and policy formulation processes, participatory online systems are a useful means of implementing public participation through informing and engaging the public to participate in spatial decision making. Web based PPGIS can involve more participants and higher degree of participation among experts, officials and the pblic than traditional means.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".