Algorithms in the historical emergence of word senses.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human language relies on a finite lexicon to express a potentiallyinfinite set of ideas. A key result of this tension is that wordsacquire novel senses over time. However, the cognitive processesthat underlie the historical emergence of new word senses arepoorly understood. Here, we present a computational frameworkthat formalizes competing views of how new senses of a wordmight emerge by attaching to existing senses of the word. We testthe ability of the models to predict the temporal order in whichthe senses of individual words have emerged, using an historicallexicon of English spanning the past millennium. Our findingssuggest that word senses emerge in predictable ways, followingan historical path that reflects cognitive efficiency, predominantlythrough a process of nearest-neighbor chaining. Our work contributesa formal account of the generative processes that underlielexical evolution.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it