A Study on the Relationship between English Vocabulary Threshold and Word Guessing Gtrategy for Pre-University Chinese Students in Malaysia
Bibliographic record
Abstract
This survey aims at studying the relationship between English vocabulary threshold and word guessing strategy that is used in reading comprehension learning among 80 pre-university Chinese students in Malaysia. T-test is the main statistical test for this research, and the collected data isanalysed using SPSS. From the standard deviation test results, the large of vocabulary is proportional to the higher score of the reading test. The standard Pearson correlation coefficient of .750, and P< .01 indicated that there is a positive relationship between word guessing and vocabulary learning. T-test results showed that the vocabulary of the students should reach about 3500 words, only then can the students consciously and flexibly use the word guessing strategy in the reading process. Based on the students’ different vocabulary level, the English teacher should provide opportunities for more learning.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".