Data Intermediation and Beyond: Issues for Web-Based PPGIS
Bibliographic record
Abstract
This article explores the implications of moving public participation GIS (PPGIS) onto the World Wide Web. It discusses the potential benefits and impediments of using the Web for PPGIS application; it then uses a PPGIS project developed solely on the Web as a case study to illustrate various issues such projects may face. It finds that the cost-benefit calculus in this transition is ambivalent: whereas some costs decrease, other threshold costs actually increase. Moving PPGIS to the Web will not undermine the traditional intermediation role of PPGIS but, rather, diversify it. The Web helps attract "occasional users" to use GIS; however, this creates new challenges for PPGIS providers, who used to work with defined clients and must now cultivate client support to anonymous clients. The Web has greatly improved connectivity and data access, which, in turn, promote collaboration among geographic and non-geographic information providers. In this context, the Web increases awareness of integrating non-geographic information such as local knowledge into GIS operations. The article concludes that Web technology alone is not sufficient to enhance the capability of every community group and resident to use GIS, to change the reality that GIS is a specialized skill, or to significantly level the unequal socio-economic or political relationships that hinder participation in distressed communities.
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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 | 0.060 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.045 | 0.079 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".