MétaCan
Menu
Back to cohort
Record W4386250985 · doi:10.22148/001c.84860

Algorithmic Maps and the Political Geography of Early-modern Japan

2023· article· en· W4386250985 on OpenAlexvenueno aff
Mark Ravina

Bibliographic record

VenueJournal of Cultural Analytics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersUniversitat Autònoma de BarcelonaCenter for Spatial and Textual Analysis, Stanford UniversityWaseda UniversityInternational Research Center for Japanese Studies
KeywordsPoliticsContiguityHistoriographyPolitical geographyRank (graph theory)GeographyState (computer science)Economic geographySpace (punctuation)PopulationCartographyGenealogyHistorySociologyPolitical scienceDemographyLawMathematicsArchaeologyComputer scienceCombinatorics

Abstract

fetched live from OpenAlex

Many of our conventional mapping practices are ill-suited to the complexities and nuances of pre-modern politics, especially in the non-Western world. Choropleth maps suggest that political borders are clear and uniform, but early modern politics was characterized by ill-defined and overlapping political spheres. This study uses interactive maps to explore the case of composite state borders in early modern Japan. Using points, rather than polygons, to represent villages, we reproduce how Tokugawa-era officials understood political space primarily as population nodes, not as clearly defined polygons. In lieu of conventional borders, we calculate Voronoi polygons to show where political authority was spatially fragmented. Using logit analysis we show that increased spatial contiguity allowed lords (daimyo) to establish monopolies and tax their holdings more intensively. Other factors, such as the lord’s rank, figure prominently in the historiography, but can not be substantiated in our analysis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cultural AnalyticsSame topicGeographic Information Systems StudiesFrench-language works237,207