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Record W2000122850 · doi:10.3138/8u64-k7m1-5xw3-2677

Full Circle: More than Just Social Implications of GIS

2005· article· en· W2000122850 on OpenAlexaffvenue
Nicholas Chrisman

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversité Laval
FundersNational Science Foundation
KeywordsRealmGeographic information systemDeterminismOpenness to experienceSociologyValue (mathematics)CriticismInformation technologyEnvironmental ethicsEpistemologySocial sciencePublic relationsPolitical scienceLawGeographyComputer scienceCartography

Abstract

fetched live from OpenAlex

The emergence of geographic information systems (GIS) has raised a useful debate in the discipline of geography over the connection between technology and society. Proponents of GIS have argued from the beginning that their work had a value that warranted adoption; hence, that technology brought something to society. A wave of criticism argued that there were implications and risks to society in adopting these technologies. While this debate served some useful purposes, it was only a start on the issue. The focus on implications risked the simplification of seeing GIS as an inexorable, implacable force, a form of “technological determinism.” This paper argues for a full circle of implication: GIS – the daily practice, the data stored, the software – is constructed and maintained by social processes embedded in historical and geographically contingent settings. The full circle requires an openness to studies of the influence from the social realm to the technology. By tracing the full circle, too, we can better appreciate the implications to society.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0080.055
Scholarly communication0.0150.028
Open science0.0020.011
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.345
Teacher spread0.321 · 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 designQualitative
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

Citations112
Published2005
Admission routes2
Has abstractyes

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Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicGeographic Information Systems StudiesFrench-language works237,207