Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This ground-breaking collection of essays examines the scope and consequences of digital vigilantism – a phenomenon emerging on a global scale, which sees digital audiences using social platforms to shape social and political life. Longstanding forms of moral scrutiny and justice seeking are disseminated through our contemporary media landscape, and researchers are increasingly recognising the significance of societal impacts effected by digital media. The authors engage with a range of cross-disciplinary perspectives in order to explore the actions of a vigilant digital audience – denunciation, shaming, doxing – and to consider the role of the press and other public figures in supporting or contesting these activities. In turn, the volume illuminates several tensions underlying these justice seeking activities – from their capacity to reproduce categorical forms of discrimination, to the diverse motivations of the wider audiences who participate in vigilant denunciations. This timely volume presents thoughtful case studies drawn both from high-profile Anglo-American contexts, and from developments in regions that have received less coverage in English-language scholarship. It is distinctive in its focus on the contested boundary between policing and entertainment, and on the various contexts in which the desire to seek retribution converges with the desire to consume entertainment. Introducing Vigilant Audiences will be of great value to researchers and students of sociology, politics, criminology, critical security studies, and media and communication. It will be of further interest to those who wish to understand recent cases of citizen-led justice seeking in their global context.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it