Bibliographic record
Abstract
Laufer and Nation (1995) proposed that the Lexical Frequency Profile (LFP) can estimate the size of a second‐language writer's productive vocabulary. Meara (2005) questioned the sensitivity and the reliability of LFPs for estimating vocabulary sizes, based on the results obtained from probabilistic simulations of LFPs. However, the underlying mathematical model for the simulations, based on Zipf's law, allows such an analysis to be done directly, without recourse to simulations. The direct analysis has the further advantage of demonstrating how variability estimates obtained from within the 1k band (the 1,000 most frequent words of English) portion of written texts may explain the simulation results. The findings confirm that the ability of LFPs to distinguish between groups diminishes as vocabulary size increases. However, for fairly homogeneous groups, LFPs are able to provide a coarse but reasonable tool for vocabulary size estimation. We also explore modifications to Zipf's law that may result in a more accurate model of word frequencies in natural language.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".