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Record W2560063018 · doi:10.3138/cart.51.4.3325

To Know the Distance: Wayfinding and Roadmaps of Early Modern England and France

2016· article· en· W2560063018 on OpenAlexvenueno aff
Christine M. Petto

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Cartography
Canadian institutionsnot available
Fundersnot available
KeywordsThrivingGovernment (linguistics)InterimState (computer science)AppealPublishingInstitutionRoad mapCartographyHistoryManagementPolitical scienceSociologyGeographyLawSocial science

Abstract

fetched live from OpenAlex

In 1675, John Ogilby produced his road atlas with strip maps which not only arrived with fanfare, but spawned several more publications that aimed to be user-friendly. As with many maps and atlases from the London printing trade, the objectives were to serve consumers, acquire a piece of the market, and have an outlet for a new edition. Across the Channel, however, the road network of France, as with other public works, was not only state-directed but a tool of state power. Not until nearly one hundred years later did Claude-Sidoine Michel and Louis-Charles Desnos produce L'Indicateur Fidèle, which provided strip maps for merchants, navigators, and travelers. This publication emerged out of the French national mapping project directed by the Cassini family. In the interim, while French map makers produced maps with an appeal to serving the state, they, like their London contemporaries, also hoped to maintain a thriving business and attract an audience, often through the traditional French social institution of patronage. The purpose of this comparative study of (post) road maps and atlases of England and France is to investigate the role of the government and the publishing trade in the production of these works.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0030.003
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.000
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 head0.008
GPT teacher head0.275
Teacher spread0.267 · 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 designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicHistorical Geography and CartographyFrench-language works237,207