Technologies and Narratives of Urban Archaeology at the Kelsey Museum
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
Urban Biographies, Ancient and Modern: Italy, Greece, Turkey, and the USA. Kelsey Museum of Archaeology, Ann Arbor, Michigan, 24 August 2018–6 January 2019, curated by Christopher Ratté with co-curators Lisa Nevett, Nicola Terrenato, Zoe Ortiz, and Kathy Velikov.The study of the city often feels as old as the city itself, and the historiography of urbanism, not only urban space, displays its own characteristic density and weight. It was thus welcome to find this small show at the Kelsey Museum, Urban Biographies, Ancient and Modern, trying something new. Rather than make yet another attempt to define the city, or to delineate urban commonalities over time, the main aim here was to present state-of-the-art technologies and methods used in the archaeological recovery of city life. The show further argued that similar methods can inform our understanding of modern urbanism. The exhibition started with three ancient sites: Gabii in central Italy, Notion on the coast of Turkey, and Olynthus in northern Greece. All three are locations of ongoing fieldwork sponsored by the University of Michigan and the Kelsey Museum, which was thus able to showcase its position as a leading academic institution for archaeological research in North America. The three sites were juxtaposed with contemporary Detroit, the large modern city near the museum and the University of Michigan's Ann Arbor campus. Both this comparison and the show's foregrounding of archaeological practices led to some interesting connections between past and present cities, while it also raised questions about how museums involved in cutting-edge archaeological research can best display their results in a gallery setting.
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 | 0.001 | 0.000 |
| 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 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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