Enquiring for Expressing Identity within Art Courses in Conventional Classes from the Diversity and Inclusion Perspective
Bibliographic record
Abstract
The multiplication of identity defining spaces in our globalized and technological society can generate tensions for individuals who are in a situation of negotiating, in different contexts, multiple identities that are sometimes perceived as incompatible (Beauregard, 2019). Indeed, each individual has several identities - these being flexible and evolving -, being at the intersection of several types of diversity, even if he is the only representative of his "category" (Ginzburg, 2003). These identities are expressed differently in the different spheres of his work and life, including school, and in each of the school subjects, including the arts. This text aims to discuss - in the context of the visual arts - the expression of identity from a perspective of diversity and inclusion. Therefore, the articulation and adaptation to the context of individual identities will be addressed, and especially the interactions between individual and social identity (Descombes, 2017). The fine arts discipline is examined as a privileged terrain of expression (Lemonchois, 2011) and sharing of sensitive experience (Rancière, 2000). How can this space of intersubjective dialogue lead to better understanding between individuals, to greater inclusion of individual diversity and multiple identities, and ultimately to an improvement in coexistence? These are the aspects mainly explored in this article.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".