Bibliographic record
Abstract
Introduction: taking to the streets in the information age / Lucas Melgaco and Jeffrey Monaghan -- Digital practices as part of social movement repertoires of contention -- Mobilisation and surveillance on social media : the ambivalent case of the anti-austerity protests in Spain (2011-2014) / Manuel Maroto and Alejandro Segura -- #rahmrepnow : social media and the campaign to win reparations for Chicago police torture survivors, 2013-2015 / Andrew S. Baer -- Cracks and reformations in the Brazilian mediascape : Midia Ninja, radical citizen journalism, and resistance in Rio de Janeiro / Tucker Landesman and Stuart Davis -- Applying privacy-enhancing technologies : one alternative future of protests / Daniel Bosk, Guillermo Rodriguez-Cano, Benjamin Greschbach and Sonja Buchegger -- Control practices of policing and security agencies -- Settler colonial surveillance and the criminalization of social media : contradictory implications for Palestinian resistance / Madalena Santos -- Between visibility and surveillance : challenges to anti-corporate activism in social media / Julie Uldam -- The impact of video tracking routines on crowd behaviour and crowd policing / Marco Kruger -- Surveillance-ready-subjects : the making of Canadian anti-masking law / Debra Mackinnon.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".