The Relationship Between Lexical Frequency Profiling Measures and Rater Judgements of Spoken and Written General English Language Proficiency on the CELPIP-General Test
Bibliographic record
Abstract
Independent confirmation that vocabulary in use unfolds across levels of performance as expected can contribute to a more complete understanding of validity in standardized English language tests. This study examined the relationship between Lexical Frequency Profiling (LFP) measures and rater judgements of test-takers’ overall levels of performance in the Speaking and Writing modules of the CELPIP-General test. In particular, the potential of measures such as lexical stretch and number of frequency bands accessed was examined. Randomized quota sampling from previously rated test-taker responses resulted in 200 speaking samples and 200 writing samples being compiled to create corpora of 211,602 running words and 70,745 running words respectively. Pearson r was used to examine the relationships between the LFP measures and rater judgements of CELPIP levels. Results point to significant correlations, with increasing CELPIP levels of performance generally accompanied by test-takers’ increasing ability to produce greater numbers of words, deploy a greater variety of words, rely less on high-frequency vocabulary, tap into mid-frequency vocabulary, and access a greater number of frequency bands. These results underline the contribution of independently obtained lexical measures toward a fuller understanding of concurrent validity in standardized English language proficiency testing. La confirmation indépendante que le vocabulaire d’usage se répand sur plusieurs niveaux de performance tel que prévu peut contribuer à une meilleure interpréta- tion de la validité des tests standardisés de langue anglaise. Ce e étude a examiné le rapport entre les mesures de profilage de la fréquence lexicale et les évalua- tions de la performance globale des élèves aux modules de parole et de rédaction du Programme canadien d’évaluation du niveau de compétence linguistique en anglais (CELPIP). Plus précisément, on a examiné le potentiel des mesures telles l’étendue lexicale et le nombre de bandes de fréquences a eintes. L’échantillon- nage par quota aléatoire de réponses d’élèves déjà évaluées a entrainé la formation de 200 échantillons de parole et 200 échantillons de rédaction représentant deux corpora, un de 211 602 mots liés et l’autre de 70 745 mots liés. On a employé le coe cient de corrélation de Pearson pour examiner les rapports entre les mesures de la fréquence lexicale et les évaluations en fonction des niveaux du CELPIP. Les résultats dévoilent des corrélations signi catives entre, d’une part, les meilleures performances au CELPIP et, d’autre part, une capacité à produire une quantité et une variété plus importantes de mots; à moins recourir aux mots les plus fréquents; à puiser dans du vocabulaire à fréquence moyenne; et à accéder à un plus grand nombre de bandes de fréquence. Ces résultats soulignent la contribution des mesures lexicales obtenues indépendamment à la compréhension de la validité concourante des évaluations standardisées des compétences linguistiques en anglais.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".