Diversity representation in advertising
Bibliographic record
Abstract
Abstract In this article we develop a comprehensive understanding of diverse representation in advertising. While numerous studies highlight increasing demand for diversity among some consumers, such enthusiasm is not universal. This is creating challenges for brands, some of which have faced backlash, either due to a perceived lack of authenticity in their diversity efforts or because not all consumer groups value diversity equally. Amidst these challenges, technological advancements, such as data-driven decision-making and generative AI, present both new opportunities and risks. The current literature on diverse representation in advertising, although expansive, is relatively siloed. Through a detailed eight-step process, we assess and synthesize the body of literature on diversity representation, reviewing 337 articles spanning research on age, beauty, body size, gender, LGBTQIA+ , physical and mental ability, and race and ethnicity. Our investigation offers two major contributions: a summarization of insights from the broader literature on these seven key areas of diverse representation and development of an integrated conceptual framework. Our conceptual framework details mechanisms, moderators, and outcomes that are either prevalent across the literature or can be reasonably expected to generalize across other forms of diversity. This framework not only offers a holistic perspective for academics and industry professionals but also exposes potential future research avenues.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".