Keepin’ it Weal in “The 514”: Hip-Hop as a Tool to Promote Well-Being in Montreal’s Marginalized Communities
Bibliographic record
Abstract
Many people today associate hip-hop with only rap music and the other commercialized aspects of the culture. However, hip-hop is, and always has been, more than simply a way to turn a profit or pass the time. Since its inception, hip-hop has aided the survival and flourishing of its progenitors and pioneers, primarily Black and Latino youth occupying the margins of society. To date, numerous scholars have examined how marginalized and oppressed people have used hip-hop to promote various aspects of well-being. While there are several ways in which well-being is perceived and defined, an important approach examines existential well-being, a philosophical approach that considers how people function along the full spectrum of human existence—physical, social, personal/psychological, and spiritual. This thesis uses existential well-being as a framework to explore how practitioners in Montreal use hip-hop to promote well-being in marginalized communities. As a form of research-creation, the core of the thesis is a series of three podcast episodes, where I explore the use of hip-hop with specific Montreal practitioners. Overall, the thesis illuminates various ways hip-hop has been used to promote well-being in Montreal and beyond. A key contribution of the thesis is to show that, while it may be useful to conceive of well-being in terms of four distinct domains, practitioners in Montreal tend to promote well-being as an integrated existential whole.
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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 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".