What to do when Teens say "Amka Ukatike": An Exploration of Agency in Teen Oral Literacy Performed Through Kenyan Hip Hop.
Bibliographic record
Abstract
This paper considers a Kenyan hip hop song; Amka Ukatike (C'zar, 2007), as an example of teen oral forms of expression and tracks how this particular song choreographs the meeting point between the canon and teen "ways with words" (Heath, 1983).Amka Ukatike translated in standard Swahili means, "get up and get cut up into pieces," but in the teenage variant form of speaking called Sheng, it means "limber up and dance till you become flexible as if your body were made up of rhythmic bits and pieces, rather than one rigid whole."Through discourse analysis of text and performance media, the paper discusses how this choreography is a metaphor of the intersection between "teenagerese" and standard school culture while demonstrating tensions in this tenuous intersection.Hip hop exemplifies teen oral literacy which is underprivileged in the formal classroom space; particularly since Kenyan hip hop is performed in Sheng, a stigmatized teenage vernacular.Ultimately, this article joins the body of knowledge that suggests the formulation of a third space as an amalgam that alleviates tensions caused by discrepancies between youth forms of oral literacy and standard school literacy (Bhabha, 1990;Soja, 1996).It is not uncommon to see the stigmatization of oral forms of literacy in schools which in this regard, have become a unique site where tensions simmer due to the clash between unacceptable teen literacies and canonical forms of literacy.Does this disconnect necessarily engender a literacy crisis?How can democracy be upheld through pedagogy that is tolerant to and inclusive of embodied and performed forms of teen oral text?This paper explores how Amka Ukatike (C'Zar 2007) teaches educators to collaborate with teenagers in creating a rich space that nurtures teenagerese while enriching the canon.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".