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Record W1940531479 · doi:10.1080/00330124.2015.1033668

Mapping Human Terrain in the<i>Joint Army–Navy Intelligence Study of Korea</i>(1945)

2015· article· en· W1940531479 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Professional Geographer · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsUniversity of British Columbia
FundersNational Geospatial-Intelligence Agency
KeywordsNavyTerrainHuman intelligenceGeographerEmpireGeospatial analysisGeographyPopulationMilitary intelligenceAdversaryCartographyHistoryOperations researchArchaeologySociologyEngineeringArtificial intelligenceDemographyComputer scienceComputer security

Abstract

fetched live from OpenAlex

The concept of human terrain has become a prominent element of U.S. military strategy. It is a means to capture the cultural–geographical qualities of an enemy or target population. An early effort to map human terrain is found in the Joint Army–Navy Intelligence Study (JANIS) of Korea (1945). We argue that the JANIS report on Korea was paradigmatic for the U.S. military's contemporary geographical work and offers insights into the cultural politics of human terrain mapping. This explains why the JANIS text is cited by the National Geospatial-Intelligence College (NGC) today as an historical model. This article not only offers a window into the history of geography counterinsurgent but also shows that geography has been entwined with empire.

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.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.108
GPT teacher head0.330
Teacher spread0.222 · 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