Bibliographic record
Abstract
Siting Futurity: The "Feel Good" Tactical Radicalism of Contemporary Culture in and around Vienna shows how cultural practitioners in and around Vienna draw on their historical knowledge of locality to create rousing productions designed to get audiences to inform themselves about useful aspects of history, to get them to engage their presents, and to help make possible more socially equitable futures. Analyses of politically engaged works of contemporary theatre, film, and photography set in and around Vienna help to identify a historically oriented mechanism that enables artists to tap into Vienna’s extraordinary, and extraordinarily under-appreciated, tradition of protest culture that dates back to the action that brought about the Wiener Neustadt “Blood Court” in the 16th century, but really came into its own with the city’s most influential occupation of an abandoned slaughterhouse for 100 days in the late summer of 1976. It also shows how work with a connection to Vienna by international stars like David Bowie, Wes Anderson, and Christoph Schlingensief has absorbed the same principles. While the overwhelming scale of technological development and the ensuing problems and crises may not have been deliberately designed to induce resignation, passivity, and despair, those who benefit from the related hyperobjects of financialization and climate change must find it convenient that they do, as demoralization reduces resistance to their profit-making machinations. It is in this context that Red Vienna’s proud tradition of social engagement and long tradition of resistance and radicality deserves to be better known.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".