MétaCan
Menu
Back to cohort

Public Participation Geographic Information Systems: A Literature Review and Framework

2006· review· en· W1987349636 on OpenAlexaff
Renée Sieber

Bibliographic record

VenueAnnals of the Association of American Geographers · 2006
Typereview
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPublic participation GISParticipatory GISGeographic information systemVolunteered geographic informationGeomaticsCitizen journalismPublic participationGIS and public healthGrassrootsEnvironmental planningInformation systemGeographyEnvironmental resource managementKnowledge managementData sciencePublic relationsComputer sciencePolitical scienceWorld Wide WebPoliticsCartography

Abstract

fetched live from OpenAlex

Public participation geographic information systems (PPGIS) pertains to the use of geographic information systems (GIS) to broaden public involvement in policymaking as well as to the value of GIS to promote the goals of nongovernmental organizations, grassroots groups, and community-based organizations. The article first traces the social history of PPGIS. It then argues that PPGIS has been socially constructed by a broad set of actors in research across disciplines and in practice across sectors. This produced and reproduced concept is then explicated through four major themes found across the breadth of the PPGIS literature: place and people, technology and data, process, and outcome and evaluation. The themes constitute a framework for evaluating current PPGIS activities and a roadmap for future PPGIS research and practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0190.030
Science and technology studies0.0010.003
Scholarly communication0.0050.008
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.051
GPT teacher head0.358
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1,187
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Association of American GeographersSame topicGeographic Information Systems StudiesFrench-language works237,207