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Record W2958298177 · doi:10.1111/cag.12554

Geography and geographic information science: An evolving relationship

2019· article· en· W2958298177 on OpenAlexvenueno aff
Michael F. Goodchild

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeospatial analysisSophisticationGeographyHuman geographyTime geographyEconomic geographyHistorical geographyData scienceContext (archaeology)Strategic geographyAgricultural geographyRegional scienceDevelopment geographySocial scienceSociologyCartographyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

GISystems have strong and longstanding roots in Geography, stemming from early developments in the 1960s and 1970s that defined a first phase of their relationship. But as the uses and sophistication of geospatial technology have grown and spread across virtually all areas of the academy, reducing Geography's claim to ownership, that relationship to Geography has evolved in new directions, forming a second phase. The critiques of the early 1990s have led to research into the societal context and social implications of GISystems that remains largely centred in Geography; techniques for the analysis of data embedded in space and time remain strongly associated with Geography; and rigorous principles have been discovered under the umbrella of GIScience that are widely recognized within and outside Geography. Today the relationship has entered a third phase, defined by the new opportunities that are being created by the growth of data science, by new sensors, and by new areas of application, suggesting that the relationship between Geography and GIScience will continue to evolve in interesting and exciting ways.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0190.016
Science and technology studies0.0060.007
Scholarly communication0.0020.011
Open science0.0010.000
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations10
Published2019
Admission routes1
Has abstractyes

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