“You Write because You Have To”: Mobilizing Spoken Word Poetry as a Method of Community Education and Organizing
Bibliographic record
Abstract
This article draws on data from a larger project that is founded on four narrative case studies that examine the ways in which Black activists in Toronto mobilize their cultural production—namely, spoken word poetry and rapping—in support of their activism, community education, and community organizing work. This particular article is founded on the work of Kofi, a pseudonym for a Toronto activist who mobilizes spoken word poetry as a method of community organizing and as a medium for Black folks to speak to their emotional lives and communal healing practices. As such, the particular narratives shared in this article continue to provide important contributions to the “new era of black words” (Fisher 2003, 362). It is through this creative labor, these activists and cultural producers address the sociology of anti-Black racism that deeply influences the lives of Afrodiasporic people in Canada. They are composers and constructors of strategies and perspectives that are founded within the historical, political, cultural, and social forces influencing Black Canada (McKittrick 2002; Austin 2013). This work continues the conversation about what it means to be Black in Canada, providing counternarratives that stand against the hegemonic and often racist ways Black people and Black communities are imagined in Canada (Austin 2013).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".