Hope in Dirt: Report of the Fort Apache Workshop on Forensic Sedimentology Applications to Cultural Property Crime, 15—19 October 2018
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".