The paper introduces representational homogeneity as a bias dimension distinct from associating groups with stereotypical attributes: it asks whether ChatGPT produces a narrower semantic repertoire for socially subordinate U.S. groups. The main study was registered on OSF before data collection and crosses four racial or ethnic labels, African, Asian, Hispanic, and White American, two genders, man and woman, and 13 short text formats. On 25 July 2023, the authors requested 500 approximately 30-word completions for each of the 104 combinations, 52,000 texts in total, using the `gpt-3.5-turbo` alias, a `chatbot` system role, and default parameters except for batches of 128, 128, 128, and 116 responses. Fifty refusals detected with six expressions were replaced; the final released dataset has exactly 500 texts per cell and no blanks. Mean length is 26.61 words (SD 2.70, range 9–68), and only 3,645 texts contain exactly 30 whitespace-separated words. The primary measure encodes each text with the penultimate layer of BERT-base-uncased and calculates all 124,750 pairwise cosine similarities among the 500 texts in each cell. It then standardizes 12,974,000 values globally and fits linear mixed models with race, gender, their interaction, and a random intercept for text format. Unregistered robustness analyses add the third-to-last BERT layer, two RoBERTa layers, and three Sentence-BERT encoders. Under BERT−2, standardized race coefficients relative to White Americans are 0.33 for African Americans, 0.31 for Asian Americans, and 0.18 for Hispanic Americans; the aggregate women-versus-men coefficient is 0.037. The BERT−2 interaction estimates women-minus-men differences of 0.0099, 0.013, and 0.12 in the three minority groups, while 0.00021 for White Americans is not different from zero under the published analysis. The subordinate-versus-White direction holds across all seven encoders and after removing race and gender words. The gender conclusion is less stable: African and Hispanic Americans retain a positive difference under all seven representations, whereas signs change by encoder for Asian and White Americans. An exploratory structural topic model finds that adversity topics dominate 41.86% of African, 26.15% of Asian, 18.65% of Hispanic, and 3.57% of White texts. Two unregistered follow-ups, 10,400 texts explicitly instructed not to mention discrimination, hardship, or adversity, and 800 stories about a chef preparing a meal for a loved one, plus within-topic comparisons for two adversity topics preserve the racial direction. This supports the claim that observed topical alignment is not the signal's only source, but it does not identify its cause. The central reservation affects all inference: the 12,974,000 rows are not independent replications. Every one of the 52,000 texts occurs in 499 pairs, so pairs sharing a text are correlated; the model includes only a format intercept and does not represent texts, dyads, or generation batches. Standard errors below 0.001, degrees of freedom near 12.97 million, z statistics in the hundreds, and follow-up tests are therefore anti-conservative. Table 2 also reports N=124,750 per intersectional group although the code combines 13 formats, 1,621,750 pairs per group, while Table 3 uses 12,974,000. The repository makes the text, prompts, and formulas auditable and is MIT-licensed, but it is not end-to-end executable as published: scripts load absent `.RData` and Feather files, no locked environments or encoder snapshots are supplied, there are no tests or CI, several paths do not match the tree, and the latest commit deleted the instructions still linked by the README. The defensible contribution is a descriptive pattern of reduced semantic diversity for three racial-minority labels in one dated ChatGPT collection, robust across several encoders and prompts. It does not establish a general LLM property, a training-data cause, equivalence to human perception, or the reported inferential precision.
Research question
Does ChatGPT generate semantically more homogeneous texts about socially subordinated racial or ethnic groups and genders than about dominant groups, and is the gender effect consistent across racial or ethnic groups?