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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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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