Word Saliency and Frequency of Academic Words in Textbooks: A Case Study in the New Standard College English
Bibliographic record
Abstract
Though textbooks are one of the main vocabulary input resources for domestic college students and core contents of learning and testing (Liu, 2013), few empirical studies are done to evaluate learning opportunities provided by textbooks. This empirical study is designed to analyze what learning opportunity is provided in a currently used series of textbooks of academic words, which in the present study are all from the 570-item Academic Word List (AWL) that Coxhead (2000) produces based on his self-constructed academic corpus. Through the interpretation of the quantitative and qualitative results, it was found that a favorable learning opportunity of academic words was provided in the number of academic word families appearing in the textbooks, their frequency distribution, and the word in-depth knowledge. The pedagogical implications were as follows: the occurrences of new words in the texts could be adjusted and controlled so as to ensure learners’ learning and use of them, and more attention should be paid to the collocation of words in the textbook designing process, which is vital to realize contextual richness and is conducive to acquire vocabulary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".