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Record W2099491566 · doi:10.1111/tgis.12089

“All the World's a Stage”: A<scp>GIS</scp>Framework for Recreating Personal Time‐Space from Qualitative and Quantitative Sources

2014· article· en· W2099491566 on OpenAlexfundno aff
Don Lafreniere, Jason Gilliland

Bibliographic record

VenueTransactions in GIS · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTraverseNarrativeSpace (punctuation)Everyday lifeQualitative researchData scienceGeographyQualitative analysisComputer scienceSociologyWorld Wide WebCartographySocial scienceEpistemologyArt

Abstract

fetched live from OpenAlex

Abstract This article presents a methodological model for the study of the space‐time patterns of everyday life. The framework utilizes a wide range of qualitative and quantitative sources to create two environmental stages, social and built, which place and contextualize the daily mobilities of individuals as they traverse urban environments. Additionally, this study outlines a procedure to fully integrate narrative sources in aGIS. By placing qualitative sources, such as narratives, within a stage‐basedGIS, researchers can begin to tell rich spatial stories about the lived experiences of segregation, social interaction, and environmental exposure. The article concludes with a case study utilizing the diary of a postal clerk to outline the wide applicability of this model for space‐timeGISresearch.

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)0.036
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.036
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0160.018
Science and technology studies0.0080.041
Scholarly communication0.0210.018
Open science0.0050.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.053
GPT teacher head0.367
Teacher spread0.314 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations34
Published2014
Admission routes1
Has abstractyes

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