Artistic Knowledge and Performance Identity Formation in Toronto's Hip-hop Communities of Practice
Bibliographic record
Abstract
This research project illustrates, through the voices of Toronto hip-hop artists, how the complex, mutually influential interactions between individuals and their communities shape and create knowledge, while encouraging the articulation of difference through unique performance identities. Their learning spaces are not institutional classrooms, but rather public spaces such as community centres, church basements, and concrete city squares, where the line between teacher and student is crossed and blurred. I employ narrative methodology as a means of obtaining the rich accounts necessary to illuminate these community-based learning processes. Hip-hop’s history is passed on orally and aurally as artists cultivate their craft. Artists’ personal stories are essential to the way hip-hop’s history, together with its teaching philosophies, are internalized and passed on in community spaces. Narratives elicited through interviews, conducted as dialogue, have the potential to more respectfully trace these individual-communal relationships. As such, the body of my data consists of the narratives of three Toronto hip-hop artists – B-boy Jazzy Jester, DJ Ariel, and MC LolaBunz – presented and interpreted according to the themes or moments that they have voiced as significant to the development of their skills and of their performance identities. The narratives presented here show the dialogic relationship between musical creativity and identity-building, resulting in embodied, performed expressions of an engagement with the tensions of lived experience. Each artist reveals their personal engagement with layers of normative discourses that are constantly at play, accepted, rejected, and creatively manipulated to fashion one’s own performance identity expressed as style.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".