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Record W1985997966 · doi:10.3138/carto.43.1.31

Complexities in Sustainable Provision of GIS for Urban Grassroots Organizations

2008· article· en· W1985997966 on OpenAlexvenueno aff
Wen Lin, Rina Ghose

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersUniversity of Wisconsin-Milwaukee
KeywordsPublic participation GISGrassrootsGIS DayGeographic information systemTraditional knowledge GISGIS and public healthSituatedEnvironmental planningProcess (computing)AM/FM/GISResource (disambiguation)Environmental resource managementBusinessGeographyComputer scienceGIS applicationsPolitical scienceCartography

Abstract

fetched live from OpenAlex

Over the past decade there has been a significant increase in the use of geographic information systems (GIS) technologies by a plethora of social groups in various fields. Public participation GIS (PPGIS) has emerged to advance more equitable access to and more inclusive use of GIS among resource-poor and traditionally marginalized community-based organizations. The issue of sustainable provision of GIS for these community groups remains critical; thus, it is worth continuing investigation, particularly with respect to unravelling the dynamic process of GIS provision. This article presents such an attempt through a critical examination of the Data Center program in Milwaukee, which has been suggested as a valuable model of GIS provision in local PPGIS practice. This study proposes that a synthesized approach of scaled network analysis helps to better explain the dynamic process of social struggle for power and control within which the GIS provision is situated. The article illustrates how multiple scaled networks have been constructed by the Data Center to facilitate its GIS provision and examines the implications of this network construction to the dynamic production of its GIS provision.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.307
Teacher spread0.288 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations22
Published2008
Admission routes1
Has abstractyes

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