Unzipping the Causality of Zipf’s Law and Other Lexical Trade-offs
Bibliographic record
Abstract
There are strong constraints on the structure of a possible lexicon. For example, the negative correlation between word frequency and length known as Zipf's law, and a negative correlation between word length and phonotactic complexity appear to hold across languages. While lexical trade-offs like these have been examined individually, it is unclear how they interact as a system. In this paper, we propose causal discovery as a method for identifying lexical biases and their interactions in a set of variables. We represent the lexicon as a causal model, and apply the Fast Causal Discovery algorithm (Spirtes et al., 1995) to identify both causal relationships between measured variables and the existence of possible unmeasured confounding variables. We apply this method to lexical data including measures of word length, frequency, phonotactic complexity, and morphological irregularity for 25 languages and find evidence of universal associations involving word length with a high likelihood of involving an unmeasured confounder, suggesting that additional variables need to be measured to determine how they are related. We also find evidence of variation across languages in relationships between the remaining variables, and suggest that given a larger dataset, causal discovery algorithms can be a useful tool in assessing the universality of lexical biases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".