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Record W2999783679 · doi:10.1007/s12520-019-00987-1

Landscapes shared by visibility: a case study on the settlement relationships of the Songgukri culture, Korea

2020· article· en· W2999783679 on OpenAlexaff
Habeom Kim, Christopher Bone, Gyoung‐Ah Lee

Bibliographic record

VenueArchaeological and Anthropological Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of Victoria
FundersKorea UniversityKorean Studies Promotion ServiceUniversity of OregonNational Science Foundation
KeywordsViewshed analysisHuman settlementVisibilitySettlement (finance)GeographyPeninsulaCultural landscapeEconomic geographyArchaeologyCartography

Abstract

fetched live from OpenAlex

Abstract The Songgukri culture (c. 2900–2400 cal. BP) in the Geum River basin is often regarded as one of the earliest complex societies in the Korean peninsula, based on some evidence for an intensified agrarian economy and social differentiation. This study focuses on landscape visibility as a method of detecting settlement relationships of the Songgukri culture. Two measures of landscape visibility, viewshed size and shared-ness of viewshed (SoV), are examined in this study. Our results indicate that while Songgukri centers tend to have larger visibility of landscape than non-centers, both centers and non-centers share their visible landscape with other settlements at a remarkably high rate. We argue that landscape visibility at Songgukri settlements reflects a shared sense of cultural belonging among settlers, rather than sociopolitical inequality between the elites in centers and the non-elites in other settlements. This study highlights a long-term process, in which bottom-up cultural interactions of Songgukri residents may have contributed to the development of settlement organization and regional communal identities over time.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.067
GPT teacher head0.286
Teacher spread0.219 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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