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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".