fashion, Modeling, and Beauty in haiti: A Space of Self-Subjectivation for Young People
Bibliographic record
Abstract
This study seeks to explore the identity formation of models in Haiti.The goal is to demonstrate how Haitian models shape their identity through fashion practices and how fashion can contribute to reinforcing their self-image.The study draws on theoretical approaches around identity and the body and applies constructivism and symbolic interaction to understand the identity construction process of the models.A qualitative methodology was used to collect and analyze the data.The research concludes that some young people in Haiti use fashion to construct and subjectivize themselves.The study categorizes the construction of the models' identity into three groups: those who want to maintain the traditional essence of modeling work, those who believe the model must manifest creativity, and those who advocate for a more relaxed fashion with less stringent body requirements. HIgHlIgHtS mIn the fashion world, modeling refers not only to a set of practices linked to its social and economic environment, but also to the work of designers and users.m The body is a medium that enables the mannequin to stage an idea, a story, a creation, a life-story.What's on display is a picture worthy of contemplation and admiration.m This study concludes that modeling work has two meanings.For some, it is a profession in which practitioners can earn their dream living.For others, it is a hobby for people who are passionate about art.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".