Is the author recognition test a useful metric for native and non-native English speakers? An item response theory analysis
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
Studies of reading have shown the "Matthew effect" of exposure to print on reading skill: poor readers avoid reading, and ability develops more slowly compared to peers, while good readers improve more quickly through increased exposure. Yet it is difficult to determine just how much an individual reads. The Author Recognition Test (ART, Stanovich & West Reading Research Quarterly, 24(4), 402-433, 1989) and its multilingual adaptations are often used for quantifying exposure to print and have shown high validity and reliability in proficient readers in their dominant language (L1). When studying bilingualism and second language acquisition, it is ideal to have a single test which is equally reliable for all cohorts for comparison, but it is unclear whether ART is effective for speakers of English as a foreign language (L2). This study assesses the reliability of ART in English-medium university and college students with different language backgrounds. Following Moore and Gordon (Behavior Research Methods, 47(4), 1095-1109, 2015), we use item response theory (IRT) to determine how informative the test and its items are. Results showed an expected gradient in ART performance, with L1 speakers showing higher scores than L2 speakers of English, university students showing higher scores than college students, and both cohorts performing better than students in an English as a second language (ESL) university pre-admission program. IRT analyses further revealed that ART is not an informative measure for L2 speakers of English, as most L2 participants show a floor effect. Reasons for this unreliability are discussed, as are alternative measures of print exposure.
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 | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 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 it