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Record W2809218142 · doi:10.3138/gsi.12.1.07

Khmer Rouge Irrigation Schemes During the Cambodian Genocide

2018· article· en· W2809218142 on OpenAlexvenueno aff
James A. Tyner, Mandy J. Munro‐Stasiuk, Corrine Coakley, Sokvisal Kimsroy, Stian Rice

Bibliographic record

VenueGenocide Studies International · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsFamineDikeGenocideGeographyIrrigationEconomic shortageWitnessCommunismTypologyArchaeologyHistoryPhysical geographyPolitical scienceGeologyLawPaleontology

Abstract

fetched live from OpenAlex

Between 1975 and 1979 Cambodia was witness to a period of mass violence in which approximately two million people died from famine, disease, and murder. This violence was the result of policies initiated by the Communist Party of Kampuchea, better known as the Khmer Rouge. To date, little research has systematically or empirically studied the geography of specific practices, notably the construction of irrigation schemes, initiated by the CPK that produced those material conditions that resulted in death and deprivation. Using satellite images, aerial photographs, archival records, and field observation, we systematically document and map Khmer Rouge irrigation schemes. Findings indicate that approximately 7,000 kilometers of canals and dikes and over 350 reservoirs were constructed during the genocide. A six-class typology is forwarded, as we argue that local hydrologic and geomorphic conditions did figure in the construction of dams, dikes, canals, and reservoirs.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.036
GPT teacher head0.359
Teacher spread0.324 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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