Hip Hop and Ya Don't Stop: Using Hip Hop to Engage Marginalized Youth in Contemporary Urban Classrooms in Canada
Bibliographic record
Abstract
This study uses two research methodologies: retrospective life histories, and qualitative research method in the form of youth questionnaires to examine student beliefs and connections to hip hop culture as a tool for student engagement. Through open-ended questionnaires with ten Canadian urban youths in the City of Toronto, this qualitative study revealed concepts of identity, student engagement, isolation and inclusion. The purpose of the study was to provide an empowering place for youth to be understood and heard in relation to their own educational journeys, capturing both the positive and negative experiences they have encountered. As a result of the study, I, the researcher was able to locate and analyze my own passion for hip hop through the retrospective life history method. Hip hop offers an array of resources, knowledge and consciousness which students can transfer across academic disciplines. This study offers recommendations for using hip hop as pedagogy to engage marginalized youth and thus lead to less isolation and more success. In order to understand hip hop’s place in schools across Canada it is important to analyze educational policies, both past and present and how these policies ultimately affect the implementation of hip hop pedagogy, which was employed in this study
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".