Bibliographic record
Abstract
As a mode of cultural production, the performance of culture – whether high or low, formal or vernacular, material or digital – involves a series of embodied actions that unfold over time and at various scales in a range of purpose-built and adaptively reused spaces. It is by moving in different ways – physically, but also imaginatively, affectively, socially, culturally and politically – that bodies, individually and collectively, produce and perform culture and hence co-generate urban spaces. Performative actions may include instruments and props, be guided by scripts and scores, and culminate in distinct situations or events. On a spectrum from amateur to professional, performers are artists (e.g., actors, poets, musicians, dancers and Carnival krewes) who present their work publicly after it has been tested and refined through rehearsal. Performances of urban culture, then, are supported by a distributed infrastructure of rehearsal spaces (as well as performance venues, the city streets and community and cultural centres) that are accessed across cities, often in a time-limited way through short-term rentals. Where they can still afford to operate in cities, rehearsal rooms and recording studios can be rented by the hour or day to individuals and small groups. Many larger cultural institutions like universities, colleges, theatres, museums, libraries, dance studios and music halls also offer rehearsal spaces through rentals and residency programmes, but these come at a cost and must be applied and budgeted for. Those performers with long-term institutional relationships and contracts usually have access to stable and affordable rehearsal facilities, but individuals and community groups often struggle to access such spaces and are forced to be more mobile. In a panoply of small rehearsal spaces in the backrooms of bars, suburban garages, strip-mall storefronts, abandoned warehouses and even on pavements, subway platforms and apartment balconies, the uncertainties and vulnerabilities underlying creativity and cultural experimentation play out. Yet, as Bingham-Hall and Kaasa (2018: 10) specify, “[i] f performers are mobile, use infrastructures for time-limited periods, and are less tied to specific locations”, their infrastructural needs are less likely to be articulated in a unified political voice that can be amplified through media coverage, and they are less likely to be implicated in the contentious “politics of place” that contributes to neighbourhood-based gentrification.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".