Doing Public Participation on the Geospatial Web
Bibliographic record
Abstract
The emergence of Web 2.0, open source software tools, and geosocial networks, along with associated mobile devices and available government data, is widely considered to have altered the nature and processes of place-based digital participation. Considerable theorizing has been dedicated to the geographic version of Web 2.0, the geospatial Web (Geoweb). To assess the theories, we draw on four years of empirical work across Canada that considers the nature of public participation on the Geoweb. We are driven by the question of how easy or difficult it is to “do” Geoweb-enabled participation, particularly participation as envisioned by researchers such as Arnstein and planning practitioners. We consider how the Geoweb could transform methods by which citizens and nonprofit organizations communicate with the state on environmental issues that affect their lives. We conduct a meta-analysis of twelve research cases and derive new findings that reach across the cases on how the Geoweb obliges us to redefine and unitize participation. This redefinition reifies existing digital inequalities, blurs distinctions between experts and nonexperts, heterogenizes the state as an actor in the participation process, reassigns participation activities in a participation hierarchy, and distances participation from channels of influence.
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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 | 0.038 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".