LexKO: A quick, reliable lexical test of Korean language proficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To facilitate objective measures of proficiency for language users of diverse backgrounds, recent research in second language acquisition and multilingualism has developed short, yet reliable, tests of lexical knowledge in a wide range of languages. In this paper, we describe the development of LexKO, a brief lexically based test of Korean language proficiency, including its underlying logic, composition, intended use, and limitations. Three rounds of pilot and validation testing with first- and second-language Korean users resulted in a highly reliable Korean test comprising 60 items that can be completed in a few minutes. Freely available for other researchers to use, LexKO produces scores that correlate significantly with both first- and second-language Korean users' scores on a standardized proficiency test (an abridged version of the Test of Proficiency in Korean) and may thus be helpful in multi-part studies for obtaining a quick, valid measure of proficiency in Korean, one of the world's fastest-growing foreign languages.
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it