Intersample variance of second-language readers should not be overlooked
Bibliographic record
Abstract
Abstract Much of the literature on first (L1) second language (L2) reading agrees that there are noticeable behavioral differences between L1 and L2 readers of a given language, as well as between L2 speakers with different L1 backgrounds (Finnish vs German readers of English). Yet, this literature often overlooks potential variability between multiple samples of speakers of the same L1. This study examines this intersample variance using reading data from the ENglish Reading Online (ENRO) database of English reading behavior comprising 27 university student samples from 15 distinct L1 backgrounds. We found that the intersample variance within L2 readers of English with the same L1 background (e.g., two samples of Russian speakers) often overshadowed the difference between samples of L2 readers with different L1 backgrounds (Russian vs Chinese speakers of English). We discuss these and other problematic methodological implications of representing each L1 background with a single participant sample.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".