Bibliographic record
Abstract
In this paper we propose a frequency-based model of vocabulary acquisition and test it on texts written by second language (L2) writers of English. One goal of the paper is to address an issue that has arisen in previous work attempting to verify Laufer and Nation’s (1995) proposal for using lexical frequency profiling tools with L2 texts to estimate the underlying vocabulary size of the L2 writers. That issue is the application of Zipf’s law (1935, 1949) directly to student texts (see Meara, 2005; Edwards & Collins, 2011), which assumes that words are learned in the order of their frequency in the language at large. As this is clearly not the case, a more valid model of vocabulary learning needs to account for the presence of less common words at different points of the acquisition process. Our model supposes that learning consists of a sequence of exposures to words, seen in proportion to their frequency in the language as a whole, and that some number of exposures are required for a word to be learned (a model parameter). This allows calculation of the probabilities that a given word (whether common or uncommon) is learned after a given number of exposures in this sequence. Furthermore, it allows calculation of the likelihood that a word is used once it has been learned, based on the word’s rank in the learner’s interlanguage (we also considered the possibility of basing this step on the word’s rank in the L2 as a whole), from which we can predict frequency distributions for learner texts. For a given 1K word count in texts, the model predicts a smaller underlying productive vocabulary than predicted by the naïve application of Zipf’s law. We then fit the parameters of the model to texts written by 90 francophone ESL learners at different points of a five-month intensive program. The best fit was obtained with a ‘number of exposures’ parameter value of 3. The model reproduces the steeper-than-Zipf tail of the frequency distribution of words observed in texts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".