Reflexive Graffiti Remixing: Curriculum, Corsican Language, and Critical Pedagogy
Bibliographic record
Abstract
Corsica is an island covered in graffiti. These painted messages devastate/decorate the walls of cities, towns, schools and homes. They have also spread to natural sites that include rocks, trees and mountains. A number of curricular questions arise in regards to graffiti as literacy and in particular regarding the corsican endangered language: Is graffiti a performance of the aesthetics of vulnerability of a minority language? Could graffitied symbols represent a linguistic affirmation of Coriscan identity? And what could engaging with these graffiti bring to canadian curricular studies regarding minority languages? The following auto/ethno/graphy attemps to answer these curricular questions as it (de)constructs a bricolage of personal photographs taken of Corsican graffiti as well as my narrative as a Canadian doctoral candidate studying in Corsica. It begins by tracing the a/r/to- and auto/ethno- graphic research framework that shapes my research and analysis. Subsequently, this paper explores various methods of researching graffiti which include reading graffiti as praxis, marginalia and empowerment. I also discuss students’ possible roles in (re)reading graffiti as an opportunity to remix and as an opening for developing critical literacy skills. In order to share my ongoing reflexive process as a researcher, my personal narrative is included throughout the paper. I have chosen to relate this narrative in French – my mother tongue and one of two minoritized languages that are at the heart of my research. This paper therefore (re)mixes English, French and Corsican throughout with as little translation possible to engage readers in a plurilingual reading experience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".