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Record W2982058888

Connecting the past to the future: A vision for reconciliation and remembrance in Anlong Veng, Cambodia

2018· article· en· W2982058888 on OpenAlexaboutno aff
Julia Mayer

Bibliographic record

VenueThe Historic Environment Policy & Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsTribunalGenocideOutreachQuarter (Canadian coin)Context (archaeology)RefugeeEconomic JusticePublishingPopulationProject commissioningDemocracyLawSociologyPolitical scienceHistoryPoliticsArchaeologyDemography
DOInot available

Abstract

fetched live from OpenAlex

During the Democratic Kampuchea regime in Cambodia, which ruled from 17 April 1975 to 7 January 1979, more than 2 million people, a quarter of the country's population estimated at the time, perished as a result of overwork, starvation, disease or execution. The Choeung Ek 'Killing Fields' and the Tuol Sleng Genocide Museum in Phnom Penh, are the two most frequently visited memorials dedicated to this period. Since 2010, there has been a significant increase in the number of local visitors to both memorials, with the Khmer Rouge Tribunal sponsoring 'study tours' in Phnom Penh. However, with the Khmer Rouge Tribunal now drawing to a close, the ongoing need for healing Cambodia's old wounds and exacting social justice has shifted from these large outreach programs designed to educate interested locals about the past, to smaller scaled activities where reconciliation is 'worked through' in the context of workshops and guided walks through history.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.016
Scholarly communication0.0070.007
Open science0.0020.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.312
Teacher spread0.294 · 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 designQualitative
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

Citations1
Published2018
Admission routes1
Has abstractyes

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