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Record W1892771595 · doi:10.51347/jum.v9i1.3914

Mapping and analysing medieval built form using GPS and GIS

2004· article· en· W1892771595 on OpenAlexaff
Keith Lilley, Christopher Lloyd, Steven Trick, Conor Graham

Bibliographic record

VenueUrban Morphology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGlobal Positioning SystemPlan (archaeology)Geographic information systemGeographyGIS applicationsComputer scienceCartographyArchaeology

Abstract

fetched live from OpenAlex

Drawing upon recent research experiences of using a Global Positioning System (GPS) and Geographical Information Systems (GIS), this paper sets out how spatial technologies can be used in the study of medieval built form. The paper focuses particularly on the use of differential GPS and ArcGIS in mapping and analysing the plan of Winchelsea, an English medieval 'new town' established in the 1280s. The approach used to conduct this research is outlined here, with comments on the practicalities of using GPS and GIS in historical urban morphology. Although the research on which this paper is based is at a preliminary stage, the paper offers a working method for those interested in using spatial technologies to build upon existing methods of morphological study, namely town-plan analysis and metrological analysis. Some preliminary research findings relating to the planning of medieval Winchelsea are also presented.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.297
Teacher spread0.257 · 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 designBench or experimental
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
Published2004
Admission routes1
Has abstractyes

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