White spaces: how marketing actors (re)produce marketplace inequities for Black consumers
Bibliographic record
Abstract
This paper interrogates how racially discriminatory practices by real estate agents, lenders, and retailers produce and reproduce marketplace inequities for Black consumers. Drawing on Critical Race Theory (CRT) and interdisciplinary research, the paper reveals the normalisation and permanence of racism in practices and policies aimed at protecting White spaces. Marketing actors racially discriminatory approaches have morphed from overt to more covert strategies, but they persist in spite of regulatory changes. Impacts on Black consumers have created profound marketplace inequities including constricted and restricted choices, devalued housing assets, housing segregation, retail discrimination, restricted and expensive access to credit, wealth gaps, and retail desertification. When viewed through a CRT lens, we conclude that in the American context, the invisible hand of the market is not invisible. Rather, it is White. The study draws implications for practice, and urgently calls for more research to unmask racism in marketing – because Black Lives Matter!
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".