"That's just not me": stylistic language and authentic dialectic identity in Canadian women MCs
Bibliographic record
Abstract
This project investigates the utilization of linguistic practices by Canadian woman MCs for identity construction and performance, aiming to further understand their social realities, experiences, and the priorities of their self-representation. Qualitative analysis of the lyrics of six notable artists (Michie Mee, Eternia, Eekwol, Tasha the Amazon, Haviah Mighty, Backxwash) finds patterns in the data for personal claims to language, territory, and ethnicity, as well as for self-assertions, Forman’s (2021) tropes of internal sensitivity and vulnerability, and positive reappropriations of derogatory terms such as bitch. By taking the comprehensive perspective of analyzing MCs’ language over time through their careers, these findings challenge existing work which has described women’s personas as reflexive of their representations by men in the genre, and which has found difficulty reconciling the authenticity of seemingly-contradictory indexical claims. Findings show that indexing duality and multi-faceted identity—including specified claims to their multicultural ethnicities—are consistent themes in the Hip-Hop of these women, and are in fact necessary for keepin’ it real. This study challenges the concept of stylization as strategic inauthenticity (Coupland, 2001) and instead introduces the notion of dialectic identity as a priority for these MCs, where multiple aspects of identity are performed without challenging one another’s authenticity. This view of women’s language in Hip-Hop encourages a renewed look at these MCs as an agentive group with their own Hip-Hop language traditions, resistant to external simplification or generalization, and concerned with nuanced self-assertion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".