Is the author recognition test a useful metric for native and non-native English speakers? An item response theory analysis
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.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".