Bibliographic record
Abstract
Cities themselves are living archives. Their built form and streetscapes are at once prosaic and visually spectacular, messy and ordered, permeable and bounded. As complex, incomplete and ever-changing entities, cities and their cultural infrastructure are the repositories of urban life (Rao 2009). Nevertheless, there are cultural institutions within cities that have explicit mandates to collect, store and exhibit memories, histories and knowledge that become the foundations of state-sanctioned culture. These range in practice from small personal collections to the activist reading rooms and archives of oppositional groups, to state-sanctioned municipal libraries, national archives and metropolitan museums. Within these collections are images, texts and material culture from the past through to the present that are catalogued, indexed and stored for selective display and reinterpretation. In cities, these repositories provide the cultural infrastructure through which to recuperate the past and reimagine urban futures. The collection of culture – the possession and assembly of rare and valuable objects – “is consumption writ large” (Belk 1995: 1). Whether compiled for archival activism or to nostalgically represent the past by refashioning new spaces and subcultures, collections make new relationships between objects, spaces, communities and their histories (Sellie et al. 2015). Collecting invariably brings objects together and, in the case of hierarchical structures like libraries, museums and archives, gives them an order in relation to one another based on classification systems (Derrida 1996). As Elsner and Cardinal (1994: 2) assert: “[i]f the peoples and the things of the world are the collected, and if the social categories into which they are assigned confirm the precious knowledge of culture handed down through generations, then our rulers sit atop a hierarchy of collections.” Collecting is a process of social display that distinguishes between things. It aspires to be distinctive and sometimes disruptive of norms while also reinforcing what constitutes taste and culture. This section focuses on the socially admissible collecting of museums, libraries and archives, attending to how this cultural infrastructure of collection serves the public good (Bain & Podmore 2020). More than just tangible institutional repositories of written, visual, sonic and material culture, they are also spaces of urban encounter across socio-cultural, ethnic and generational divides that are embedded in locales (Amin 2008).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.033 |
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".