Representations of Black Female Bodies in Contemporary Caribbean Art: A Model for Responding to Art
Bibliographic record
Abstract
Drawing on critical race theory, intersectionality, and gender, I examine representations of Black female bodies in contemporary Caribbean art by examining three paintings by Barbadian artist Sheena Rose: (a) Black Beauty, (b) Anxiety, and (c) Mental Illness, Don’t Laugh!!! It Isn’t Funny!! Shouted the Angry Artist. I aim to encourage students and educators to consider their perceptions on issues such as race, gender, and mental health when viewing art by Black female artists. Due to the tension between observation in formal analysis and the focus on lived experiences and feelings in phenomenology, I created a Self-Reflective Model for Art Criticism Featuring a Pedagogy of Identity Exploration, resulting in a teaching unit for visual arts by Black female artists using Sheena Rose’s artwork. Due to the current and historical racialized, sexualized representations of Black Female bodies, my research is significant because of its relevance to art history, pedagogy, and studio art settings beyond Trinidad and Tobago and Canada. It also allows students to gain a deeper understanding and appreciation for diversity and cultural knowledge in high school classrooms and other art settings.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".