Pride, not prejudice: a cross-cultural big data analysis of consumer reactions to rainbow-washing in advertising
Bibliographic record
Abstract
Rainbow-washing in advertising has become a critical issue, highlighting the gap between superficial LGBTQIA+ advocacy and brands’ lack of genuine commitment to the community. Using queer narrative theory, this study utilized a big data approach to investigate 128,520 consumer Reddit posts about rainbow-washing from non-Western (India, Indonesia, Malaysia, South Africa) and Western (USA, UK, Australia, Canada) cultures. The findings reveal both micro-level linguistic narratives and macro-level cultural differences in how consumers react to rainbow-washing. This research deepens our understanding of consumer activism against ‘woke’ brands, contributing to both intercultural and narrative-based theories of advertising. Practically, it urges advertisers to go beyond Pride Month campaigns and engage authentically with LGBTQIA+ communities year-round, focusing on both internal and external brand expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".