Global Linguistic Flows: Hip Hop Cultures, Youth Identities, and the Politics of Language
Bibliographic record
Abstract
@contents: Selected Contents INTRO Outta Compton, Straight aus Munchen: Global Linguistic Flows, Identities, and the Politics of in a Global Hip Hop - H. Samy Alim DISC ONE Styling locally, styling globally: The Globalization of and Culture in a Global Hip Hop Nation TRACK ONE as Dusty Foot Philosophy: Engaging Locality - Alastair Pennycook and Tony Mitchell TRACK TWO Language and the Three Spheres of - Jannis Androutsopoulos TRACK THREE Conversational Sampling, Race Trafficking, and the Invocation of the gueto in Brazilian - Jennifer Roth-Gordon TRACK FOUR 'You shouldn't be rappin, you should be skateboardin the X-games': The Co-construction of Whiteness in an MC Battle - Cecelia Cutler TRACK FIVE From Da Bomb to Bomba: Global Hip Hop Nation in Tanzania - Christina Higgins TRACK SIX 'So I choose to do am Naija style': Hip-Hop, and Postcolonial Identities - T. Omoniyi DISC TWO The Power of the Word: Hip Hop Poetics, Pedagogies, and the Politics of in Global Contexts TRACK SEVEN 'Still reppin por mi gente': The Transformative Power of Mixing in Quebec - Mela Sarkar TRACK EIGHT 'Respect for da chopstick Hip Hop': The politics, Poetics, and Pedagogy of Cantonese Verbal Art in Hong Kong - Angel Lin TRACK NINE Rhyme and the Reinterpretation of Hip Hop in Japan - Natsuko Tsujimura and Stuart Davis TRACK TEN 'That's all concept it's nothing real': Reality and Lyrical Meaning in Rap - Michael Newman TRACK ELEVEN Creating 'an empire within an empire': Critical Hip Hop Pedagogies and the Role of Sociolinguistics - H. Samy Alim TRACK TWELVE Takin Hip-Hop to a Whole Nother Level: Metissage, Affect and Pedagogy in a Global Hip-Hop - Awad Ibrahim HIP-HOP HEADZ aka LIST OF CONTRIBUTORS
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".