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Record W2969634943 · doi:10.1017/s0940739119000092

Hope in Dirt: Report of the Fort Apache Workshop on Forensic Sedimentology Applications to Cultural Property Crime, 15—19 October 2018

2019· article· en· W2969634943 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

VenueInternational Journal of Cultural Property · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLaw enforcementSedimentologyArchaeologyDirtCriminologyLootingHistoryPolitical scienceSociologyLawGeographyGeologyCartographyPaleontology

Abstract

fetched live from OpenAlex

Summary: A 2018 workshop on the White Mountain Apache Tribe lands in Arizona examined ways to enhance investigations into cultural property crime (CPC) through applications of rapidly evolving methods from archaeological science. CPC (also looting, graverobbing) refers to unauthorized damage, removal, or trafficking in materials possessing blends of communal, aesthetic, and scientific values. The Fort Apache workshop integrated four generally partitioned domains of CPC expertise: (1) theories of perpetrators’ motivations and methods; (2) recommended practice in sustaining public and community opposition to CPC; (3) tactics and strategies for documenting, investigating, and prosecuting CPC; and (4) forensic sedimentology—uses of biophysical sciences to link sediments from implicated persons and objects to crime scenes. Forensic sedimentology served as the touchstone for dialogues among experts in criminology, archaeological sciences, law enforcement, and heritage stewardship. Field visits to CPC crime scenes and workshop deliberations identified pathways toward integrating CPC theory and practice with forensic sedimentology’s potent battery of analytic methods.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.546
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.323
Teacher spread0.267 · 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