Cultural Landscape Studies Help Match Cultural Resource Identification and Assessment Efforts to Undertaking Size and Complexity in the Section 106 Process
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Section 106 of the National Historic Preservation Act requires US federal agencies and their applicants to consider historic properties affected by their proposed actions. Guided principally by architectural historians and archaeologists throughout the 1980s, Section 106 reviews focused on identifying discrete structures and sites and then evaluating them in terms of dominant society aesthetics, histories, and sciences. By the 1990s, Section 106 participation by consulting Tribes and other cultural resource stewards obliged federal agencies to address a broader spectrum of historic properties and values. Agencies soon began using cultural landscape studies and other research and consultation tools to “match” historic property identification and assessment processes to the scale and complexity of proposed undertakings. The Section 106 review for the SunZia interstate transmission line (2009–2024) shows that the federal government has yet to consistently meet mandates to identify and assess elements other than archaeological/architectural historic properties. Our surveys of historic preservation professionals and available cultural landscape studies underscore disconnections between practitioner preferences for and the federal agency conduct of cultural landscape studies. They also highlight standards to use in evaluating the adequacy of cultural landscape studies. We recommend six attributes as essential to all cultural landscape study designs, methods, and applications in the Section 106 process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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