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Geo‐Pragmatics for the Geospatial Semantic Web

2007· article· en· W2035317460 on OpenAlexaff
Boyan Brodaric

Bibliographic record

VenueTransactions in GIS · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsGeological Survey of Canada
FundersDivision of Ocean Sciences
KeywordsPragmaticsGeospatial analysisContext (archaeology)SituatedOntologyComputer scienceIdentification (biology)Semantic WebData scienceRepresentation (politics)Meaning (existential)EpistemologyWorld Wide WebGeographyArtificial intelligenceLinguisticsCartographyArchaeologyEcology

Abstract

fetched live from OpenAlex

Abstract Geo‐Pragmatics is introduced here as an enhanced representation for ontologies in which geospatial, geographical and geoscientific concepts are not only defined, but their pragmatic context is also captured and potentially reasoned with. A framework for representing such context is developed using three core aspects: dimensions, agents and roles. Dimensions consist of a concept's origins, uses and effects; these are generated by the interaction of human, machine and natural agents, and involve entities with roles developed from method‐driven perspectives and epistemic‐driven versions. The relationship between these core aspects is explored conceptually and implications for geoscientific ontologies are discussed, including identification of a basic ontological type, the situated concept, whose meaning is defined by its geographical‐historical context. Geo‐pragmatics should help geoscientists evaluate the scientific merit, and fitness for scientific use, of geoscientific ontologies in emerging e‐science initiatives.

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)0.009
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0050.013
Scholarly communication0.0100.018
Open science0.0020.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.025
GPT teacher head0.318
Teacher spread0.293 · 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
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

Citations40
Published2007
Admission routes1
Has abstractyes

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