Introduction: configuring urban cultural infrastructure
Bibliographic record
Abstract
INTRODUCTION Cities are works of art. They are imaginative objects. They have life forces of their own that bring into view colliding individual and collective needs and ambitions and “heterogeneous views of functions and requirements of administering, of instituting, and distributing resources” (Blum 2003: 5). As centres of commerce, transportation and government, where large numbers of people live and work, cities are dense gathering places, sites where cultures intersect and collide (Bain & Peake 2022). It is from the emblematic co-presence of strangers, social interactions and critical engagements that collective life is built through institutions, public spaces, workplaces and homes within neighbourhoods (Miles 2007). But that density also brings with it socio-political conflicts, inequality brought by struggles over resources and infrastructure, as well as the threat of terrorism and disease transmission (Anheier et al. 2021). In recent years, the global Covid-19 pandemic has, within a short period of time, produced a counter to the historical role of cities as sites of population concentration and social interaction. It has spurred urban exoduses and the digitization of many urban education and medical institutions, workplaces and retail and entertainment environments. Through information communication technologies (ICTs), the domestication of urban social relations has intensified, potentially eroding and reconfiguring long-established public-private spatial binaries that inform the meanings of urban places and the social structures and morphologies of cities. While ICTs enable spatiotemporal transcendence, meaning that “fewer relationships or transactions require … copresence”, they also increase the capacity for centralized surveillance and social control from various corporate and state actors (Calhoun 1992: 221). A constrained urban public life is concomitantly the product of this dramatic and rapid reworking made possible by the continual malleability of the material and human cultural infrastructure of cities that are ever-open to such change. Often treated as superficial in contrast with the frameworks of urban economies, material and human cultural infrastructure is what makes cities themselves archives and works of art. In their diversity and multiplicity, cities are also synonymous with the production and consumption of culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".