Bibliographic record
Abstract
Cultural production – its services, activities and networks – are embedded in, and transformative of, places. Within cities, residential, industrial and commercial neighbourhood sites that fall into disrepair are revalorized through creative activity (Rantisi & Leslie 2010). Contemporary urban planning and policy solutions have leveraged the culture-capital compromise wherein the “authenticity” of artistic labour and the appeal of creative lifestyles (Zukin 1982) are employed to breathe new life into decaying urban industrial infrastructure – store fronts, strip malls, schools, religious institutions, factories and warehouses (Zukin 2010). Sites of cultural production – where the process of artistic creation and art-making result in the fabrication of objects or activities – are often mythologized in the public imagination, yet they have straightforward infrastructural requirements. Spaces of cultural production demand the permanent storage of, and access to, tools, equipment and materials which suggests the need for stable locations that do not necessarily require the co-presence of audiences (Bingham-Hall & Kaasa 2018). Art schools are one such key site of cultural production, that resemble laboratories or factories of research, experimentation and innovation. In art schools, emerging communities of artists work at the avant-garde edge of their disciplines, looking “to the past and tradition for inspiration” but their “main currency” is “hip coolness, progressive ideas and place in the contemporary art scene” (Becker 2009: 38). Within this world of culture-making, it is the makers themselves who are highly esteemed, and those cultural workers – those artists – who gain critical attention for their labour who are most admired, along with the spaces they inhabit (Becker 2009). Beyond art schools, art studios have long been privileged as sites for undisturbed experimentation with materials, sound, light, movement and ideas that constitute the making of cultural work as well as the formation of professional identities (Bain 2004, 2005). The studio is celebrated as a “space for ongoing as well as finished projects, colour, paintings, scraps, scribbles, prototypes, ideas, chaos, order, language” (Sjöholm 2014: 505– 6). As a site to showcase oneself to curators and collectors, studios are not just places to make and store artwork. Rather, artists are collectors of objects and archivists of their own work and studios become places “where careers are stored and developed” and “where artists look both forward and backward in their practice” (Hawkins 2017: 91).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.030 |
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".