A Close Look at the Relationship between Multiple Choice Vocabulary Test and Integrative Cloze Test of Lexical Words in Iranian Context
Bibliographic record
Abstract
In spite of various definitions provided for it, language proficiency has been always a difficult concept to define and realize. However the commonality of all the definitions for this illusive concept is that language tests should seek to test the learners’ ability to use real-life language. The best type of test to show such ability is considered to be the integrative test and cloze test is in turn regarded to constitute nearly all the factors needed for language use ability. However, the greatest obstacle of cloze tests or in general pragmatic tests is their administrative and scoring constraints for a large number of testees. Discrete-point tests, as the easiest and most common type of tests used for valid national and international proficiency tests, have always been doubtfully questioned as to whether they indicate the learners’ ability to use language in real-life situation, but due to their tangible shortcomings, no absolute answer can be provided. The study aims at shedding light at the idea of the extent to which the discrete items of vocabulary proficiency show the learners’ vocabulary proficiency in the real world of language use. Hence, the study seeks to calculate the correlation between discrete-point and integrative language proficiency tests of vocabulary administered to 21 Iranian freshmen studying English as a Foreign 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".