Toward a model of language acquisition threshold
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
We demonstrate how the paradigm of complex networks can be used to model some aspects of the process of second language acquisition. When learning a new language, knowledge of 3000-4000 of the most frequent words appears to be a significant threshold, necessary to transfer reading skills from L1 to L2. We show that this threshold corresponds to the transition from Zipf's law to a non-Zipfian regime in the rank-frequency plot of words of the English language. Using a large dictionary, we then construct a graph representing this dictionary, and study topological properties of subgraphs generated by the k most frequent words of the language. The clustering coefficient of these subgraphs reaches a minimum in the same place as the crossover point in the rank-frequency plot. We conjecture that the coincidence of all these thresholds may indicate a change in the language structure, which occurs when the vocabulary size reaches about 3000-4000 words.
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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 | 0.000 | 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) | 0.000 | 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 it