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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it