CATALOGING PROTEST: NEWSPAPERS, NEXIS UNI, OR TWITTER?*
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
What is the best source for tracking protest activity? Newspaper sources remain dominant, but other options are tempting. This article compares three differently sourced catalogs of protest events in Toronto from July 15 to September 15, 2020. The widely discussed Movement for Black Lives and housing justice cycles of protest are visible in all three catalogs, but apart from this, the field of protest they reveal is very different. While the coverage by the newspaper with the largest circulation, the Toronto Star, shows Toronto protest as state-centered, domestic, and progressive, other catalogs that include television, radio, and social media content reveal a more diverse, fragmented, and globalized protest field. Catalogs sourced from Nexis Uni and Twitter show the significant presence of diasporic protest. These observations suggest new limits to relying on mainstream newspapers for representing the full array of protest activity. We recommend that, moving forward, researchers experiment with media aggregators to incorporate sources such as television coverage and social media into their research while remaining aware of the additional challenges such data generate.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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