Black Lives Matter Toronto: A Qualitative Study of Twitter’s Localized Social Discourse on Systemic Racism
Bibliographic record
Abstract
This Major Research Paper examines the Twitter discourse of Black Lives Matter Toronto (BLMTO), a chapter of the Black Lives Matter Movement which addresses issues of racism and police brutality. BLMTO protested in front of police headquarters between April 1st and April 15th, 2016 and used Twitter to document their protest during this time. This paper provides a content and sentiment analysis of 346 tweets collected during this time frame. The analysis of the Twitter content is based on concepts drawn from the scholarly literature on the public sphere, identity and social identity, and framing theory. My findings indicate the following: Black Lives Matter Toronto uses media framing techniques, as well as logical and moral appeals, to build credibility as a strong subaltern counterpublic, an information resource for community building and an influencer online, through sharing relevant statistics, news stories and persuasive rhetoric. BLMTO incorporates calls to action to create publicity and facilitate community mobilization. Key themes in the tweets include the exercise of power in society, the need to build community and create a common sense of right and wrong, and maintaining solidarity
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".