Bibliographic record
Abstract
Cross-disciplinary analysis of contemporary images and representations of hysteria\nWe seem to be living in hysterical times. A simple Google search reveals the sheer bottomless well of “hysterical” discussions on diverse topics such as the #metoo movement, Trumpianism, border wars, Brexit, transgender liberation, Black Lives Matter, COVID-19, and climate change, to name only a few. Against the backdrop of such recent deployments of hysteria in popular discourse––particularly as they emerge in times of material and hermeneutic crisis––Performing Hysteria re-engages the notion of “hysteria”.\nPerforming Hysteria rigorously mines late 20th- and early 21st-century (primarily visual) culture for signs of hysteria. The various essays in this volume contribute to the multilayered and complex discussions that surround and foster this resurgent interest in hysteria––covering such areas as art, literature, theatre, film, television, dance; crossing such disciplines as cultural studies, political science, philosophy, history, media, disability, race and ethnicity, and gender studies; and analysing stereotypical images and representations of the hysteric in relation to cultural sciences and media studies. Of particular importance is the volume's insistence on taking the intersection of hysteria and performance seriously.\nContributors: Johanna Braun (University of Vienna), Vivian Delchamps (University of California), Cecily Devereux (University of Alberta), Sander L. Gilman (Emory University), Elke Krasny (Academy of Fine Arts Vienna), Jonathan W. Marshall (Edith Cowan University, Western Australian Academy of Performing Arts), Sean Metzger (University of California), Tim Posada (Saddleback College), Elaine Showalter (Princeton University), Dominik Zechner (Brown University / Rutgers University)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.091 | 0.000 |
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 teacher head, 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".