Intersections between visual design and representation: An evaluative study of the ethical dilemmas in the production of HIV/AIDS advocacy advertisements
Bibliographic record
Abstract
HIV/AIDS advocacy advertisements often use constructions of specific cultural groups to communicate the need for immediate action to prevent the spread of the virus. This study examines how graphic design strategies such as the use and juxtaposition of colour, photography, typography and vectors create representations of cultural identities. A selection of documents collected from The AIDS Committee of Toronto, International AIDS Day 2009 and The Stephen Lewis Foundation were the sites of the analysis. Drawing from theories of cultural studies and philosophy, this research project examined the semiotic strategies of the documents to develop a set of ethical best practices for visual design. Issues including the representation of cultural groups through victimage, as well as the pace at which an audience is presented information, were key in understanding ethical challenges the visual design of these documents present. The following set of best practices were developed to account for the emerging conventions and moral dilemmas identified in the study: i) Recognizing the harm of victimizing groups, ii) Developing visual representations that avoid negatively stereotyping groups, and iii) Accurately explaining HIV/AIDS issues and its prevention rather than relying on narratives.
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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 | 0.050 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".