Bibliographic record
Abstract
Catastrophes unsettle our safe places within the world. As such, they provide an interesting site to analyze the intersection of our affective and political lives. Bringing radical democratic thinking, affect theory, psychoanalysis, and discursive analysis to bear on contemporary catastrophic events, Democracy and Event presents a fresh perspective on the study of affect and its impact on democratic sensibilities and practices. Situated in different countries with differing institutional histories and cultures – the Grenfell Tower fire in London, England (2017); the SARS epidemic in Toronto, Canada (2003); the Parkland shooting in Florida (2018); the early days of the COVID-19 crisis and the murder of George Floyd in Minneapolis, USA (2020) – Elaine Stavro interprets the rhetoric, discourse, and affective communication of politicians and passionate protestors. She examines their linkages to well-established organizations informed by democratic ideals, as well as the context in which they arise, which have a bearing on their ability to challenge neoliberal and authoritarian practices. Inspired by the urgent need to bring theory back to politics and politics back to theory, Elaine Stavro demonstrates how theory might inform our attitudes to contemporary events while recognizing that political action and events cannot be captured in their complexity by theory. Her skillful engagement with various theoretical approaches, read through the lens of catastrophic events, will speak to a wide-ranging scholarly readership in numerous academic fields.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".