Cultural Resource Damage Assessment
Bibliographic record
Abstract
ABSTRACT Unauthorized cultural resource alterations range from looting and grave robbing to contract violations and wildland fires. Such alterations degrade cultural resources’ spiritual, communal, ecological, economic, and scientific values. Alterations often violate communal senses of place, security, and belonging. Alterations complicate jurisdiction-specific management, which is premised on up-to-date information on resource sizes, conditions, and significance. Cultural resource damage assessment protocols based on proven forensic practices distil to eight fieldwork steps: verify the alteration, assemble the team, survey the scene, document the evidence, gather the evidence, assess the archaeological value and the cost of repair and restoration, prescribe emergency remediation, and confirm evidence documentation and custody. The eight steps give special consideration to local communities and Indigenous Territories, where unauthorized alterations are as common as they are elsewhere, whereas impacts to spiritual and cultural values are generally greater. Adapted to jurisdiction- and incident-specific circumstances, the steps will guide responses to alterations by community leaders, land managers, regulators, law enforcement agents, and archaeologists, including preparation of excellent damage assessment reports. Damage assessment practitioners and land managers should refine these practices to deter alterations, engage Tribes and other affected communities, halt postalteration degradation, ensure accountability, and enable jurisdiction-scale curation of cultural resources and their unique value constellations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".