# Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat

arXiv: https://arxiv.org/abs/2003.12133 · HTML: https://mlsanity.com/paper/2003.12133/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Integrating topic modeling and word embedding to characterize violent deaths](https://mlsanity.com/paper/2106.14365/) (2021)
- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)
- [Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency](https://mlsanity.com/paper/2201.08451/) (2022)
- [A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies](https://mlsanity.com/paper/2506.21587/) (2025)
- [The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instruments](https://mlsanity.com/paper/2603.10130/) (2026)
- [Dialectograms: Machine Learning Differences between Discursive Communities](https://mlsanity.com/paper/2302.05657/) (2023)
- [Moral consensus and divergence in partisan language use](https://mlsanity.com/paper/2310.09618/) (2023)

## Abstract (arXiv)

As we navigate our cultural environment, we learn cultural biases, like those around gender, social class, health, and body weight. It is unclear, however, exactly how public culture becomes private culture. In this paper, we provide a theoretical account of such cultural learning. We propose that neural word embeddings provide a parsimonious and cognitively plausible model of the representations learned from natural language. Using neural word embeddings, we extract cultural schemata about body weight from New York Times articles. We identify several cultural schemata that link obesity to gender, immorality, poor health, and low socioeconomic class. Such schemata may be subtly but pervasively activated in public culture; thus, language can chronically reproduce biases. Our findings reinforce ongoing concerns that machine learning can also encode, and reproduce, harmful human biases.