A new method for computational cultural cartography: From neural word embeddings to transformers and Bayesian mixture models
Bibliographic record
Abstract
Recently, computational social scientists have proposed exciting new methods for 'mapping meaning space' and analysing the structure and evolution of complex cultural constructs from large text datasets. These emerging approaches to 'cultural cartography' are based on a foundation of neural network word embeddings that represent the meaning of words, in relation to one another, as vectors in a shared high-dimensional latent space. These new methods have the potential to revolutionize sociological analyses of culture, but in their current form, they are dually limited. First, by relying on traditional word embeddings they are limited to learning a single vector representation for each word, collapsing together the diverse semantic contexts that words are used in and which give them their heterogeneous meanings. Second, the vector operations that researchers use to construct larger 'cultural dimensions' from traditional embeddings can result in a complex vector soup that can propagate many small and difficult-to-detect errors throughout the cultural analysis, compromising validity. In this article, we discuss the strengths and limitations of computational 'cultural cartography' based on traditional word embeddings and propose an alternative approach that overcomes these limitations by pairing contextual representations learned by newly invented transformer models with Bayesian mixture models. We demonstrate our method of computational cultural cartography with an exploratory analysis of the structure and evolution of 120 years of scholarly discourse on democracy and autocracy.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".