I Too Know Why The Caged Bird Sings: Rapping and Spoken Word as Activism and Education
Bibliographic record
Abstract
ABSTRACT I have often witnessed how racialized children, youths, and young people have mobilized these practices through their cultural production. Such actions have served to (re)imagine what constitutes literacy, thus making space for more complex, fluid, and robust understandings of the literacy practices of Black youth (Fisher, 2003; Kinloch, 2010; Kirkland, 2013; Rowsell, 2011). The project is a collection of four narrative case studies that examined how four young Black men in the Greater Toronto Area employ spoken word poetry and rapping in their education and activism work. Drawing on New Literacy Studies (e.g., Street, 2003), the rhetoric of cultural production (Gaztambide-Fernández, 2013), and Critical Race Theory (e.g., Ladson-Billings Tate, 1995), I explore how they each created out-of-school educational workshops as a respond to the low self-esteem, depression, and marginalization as well as the creative potential of the racialized youth and children they support in the schools and communities they work within. As such, the two key questions that guided this research are: (a) How do these four Black young men explain their choice to use spoken word poetry and rapping to express their lived realities, including the systemic racism they continue to live with? (b) How do these four Black young men use spoken word poetry and rapping to educate themselves and their communities? Three key themes emerged from the narrative case study data. First, these Black young men engage in the cultural production of rapping and spoken word poetry as a method of expressing and theorizing their emotional lives and lived realities. Their cultural production helped them make sense of and cope with the pain and difficulties they have experienced. Second, the cultural production of these young men counters problematic, monolithic conceptualizations of Black people in Canada. Participants’ critical and creative labour provide counter-narratives to the stereotypical ways that Black youth are regarded. Third, findings highlight how Ebele, Kofi, TD, and Efe created a “new” form of education that supports the healing of racialized children and youths in urban communities, as alternatives to formal educational institutions that often marginalize Black children and youths.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".