« C’est pas about toi, c’est about moi » : l’acadjonne, le rap et l’intertextualité dans la construction identitaire du rappeur acadien Jacobus
Bibliographic record
Abstract
This thesis analyzes elements which contribute to the construction of the artistic identity of the Acadian rap artist Jacobus. Nowadays, many artists perform on the local and international stages from musical, social or linguistic margins. Their success is due to the democratization of production and music broadcasting tools. As this phenomenon becomes more and more common and popular, “marginal” artists and their communities blur the lines between the mainstream and the underground, by the means of performing in their vernacular and promoting these authentic language practices. Jacobus sings in Acadjonne, a variety of Acadian French spoken in la Baie Sainte-Marie, Nova Scotia. By singing in his vernacular, consciously or not, he claims his Acadian identity, as other artists do so with Chiac, another variety of Acadian French (for example, Lisa LeBlanc and Les Hay Babies). Jacobus, as other artists, claims and proclaims his Acadian identity while promoting his vernacular, which has provoked controversial discussions in the media. Through his songs, the artist destroys the stereotypes of rap music. At the same time, he transgresses the linguistics norms by choosing the linguistic minority over the proper, standard French. In this thesis, I analyze the songs from Jacobus’ two solo albums and various aspects of his songs that contribute to the construction of a “marginalized” and authentic artistic identity. This research shows that the artist’s linguistic practices and the fact that he brings the Acadjonne variety on the Québécois, Canadian or even global stages, contribute to the construction of his authentic identity, but also to the spreading of local varieties of French language, which goes against the linguistic imperialism of the ideology of the standard. This linguistic behaviour enhances the actual societal conversation about inclusion and diversity in Canada.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".