Cultural Landscape Studies Help Match Cultural Resource Identification and Assessment Efforts to Undertaking Size and Complexity in the Section 106 Process
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.
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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".