The Lexical Breadth of Undergraduate Novice Level Writing Competency
Bibliographic record
Abstract
Abstract This study builds on previous work exploring reading and listening lexical thresholds (Nation, 2006; Laufer & Ravenhorst-Kalovski, 2010; Schmitt, Jiang, & Grabe, 2011) in order to investigate productive vocabulary targets that mark successful entry-level undergraduate writing. Papers that passed the Effective Writing Test (EWT) were chosen to create a corpus of novice university level writing (N = 120). Vocabulary profiles were generated, with results indicating the General Service List (GSL) and the Academic Word List (AWL) cover an average of 94% of a typical paper. Further analysis pointed to 3,000 word families and 5,000 word families covering 95% and 98% respectively of each paper. Low frequency lexical choices from beyond the 8,000 word family boundary accounted for only 0.6% coverage. These results support the frequency principle of vocabulary learning (Coxhead, 2006), and provide lexical targets for English for Academic Purposes (EAP) curriculum development and materials design. Résumé Cette étude s'appuie sur des travaux antérieurs qui explorent les niveaux lexicaux pour la lecture et l’écoute (Laufer et Ravenhorst-Kalovski, 2010; Nation, 2006; Schmitt, Jiang et Grabe, 2011). Elle a pour but d'étudier les niveaux de production lexicale qui marquent l'écriture à l'entrée à l'université anglophone. Pour créer un corpus d'écriture de niveau universitaire novice, 120 articles qui ont passé le Effective Writing Test (EWT) ont été choisis. Des profils de vocabulaire ont été générés et les résultats signalent que la General Service List (GSL) et la Academic Word List (AWL) couvrent une moyenne de 94% d'un document typique. En plus, 3 000 familles de mots et 5 000 familles de mots couvrent 95% et 98% respectivement de chaque article. Les choix de basses fréquences lexicales au-delà de la limite de 8 000 mots ne représentaient que 0,6% de la couverture. Ces résultats appuient le principe fréquence de l'apprentissage du vocabulaire (Coxhead, 2006) et fournissent des niveaux lexicaux pour les programmes d’anglais à des fins académiques.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".