Bibliographic record
Abstract
In 1979, the Educational Testing Service (ETS) developed the TOEIC (Test of English for International Communication), an English proficiency test for people working in international environments, based on a request from the Japanese Ministry of International Trade and Industry. The Chauncey Group International, a subsidiary of ETS, currently develops and publishes the test. Over two million people per year take the TOEIC (www.toeic.com). According to the TOEIC Report on Test-Takers Worldwide, 1997-98, 63% of the TOEIC results were used in Japan, 29% in Korea, and 8% in other countries. Most reviews of the TOEIC have been descriptions of the test (Gilfert, 1996; Perkins, 1987). The TOEIC comprises the listening and reading section. Buck (2001) reviews only the listening section. For the reading section of the TOEIC we could find only one critical review (Richards, 1992) published over the two decades since the test was developed. Therefore, our purpose in this article is to review critically the reading section based on recent studies of language assessment, particularly for construct validity and content validity, which are considered by language testing researchers (Backman, 1990; Cumming, 1996) as fundamental for validation of language tests.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".