Bibliographic record
Abstract
To listen to hip hop is to enter a world of complexity and contradiction. —Imani Perry (2004, p. 1) Over the past five years I have directed and developed, in collaboration with my Interactive Media and Performance (IMP) Labs ’ research team, artists, teachers, elders, community partners, and youth, a number of community hip hop arts-based projects in Saskatchewan. These community-based programs have been shaped by the following research questions: What role does hip hop play in narrating settler/colonialism on the prairies or in the north? What happens to stories when they are (re)told through a contem-porary oral practice and mediated by the discourses associated with hip-hop cul-ture on a global scale? How does hip hop challenge contemporary Canada to think about “Aboriginal ” politics and colonial-ism in the present and the future, rather than framing them as only relevant to the past? How does Indigenous hip hop com-plicate the spirit of a liberal pluralist soci-ety such as Canada?1 Four years and nine Hip Hop projects later,2 these questions, although still relevant and necessary, no longer capture how I have come to understand and theorize hip hop as a methodology, or as a conceptual model for researching and articulating ways of knowing (Covach, 2010). Transitioning away from the conventional approach to theorizing community-based arts projects as a discourse of intervention (e.g., by tar-geting “at risk ” youth), I argue the Hip Hop Projects facilitate a recognizable sense of
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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 | 0.049 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".