An automated version of the BAT Syntactic Comprehension task for assessing auditory L2 proficiency in healthy adults
Bibliographic record
Abstract
Studies of bilingualism sometimes require healthy subjects to be assessed for proficiency at auditory sentence processing in their second language (L2). The Syntactic Comprehension task of the Bilingual Aphasia Test could satisfy this need. For ease and uniformity of application, we automated its English ( Paradis, M., Libben, G., and Hummel, K. (1987) . The Bilingual Aphasia Test. English version. Hillsdale, NJ: Lawrence Erlbaum Associates) and French ( Paradis, M., & Goldblum, M. C. (1987) . The Bilingual Aphasia Test, French version. Hillsdale, NJ: Lawrence Erlbaum Associates) versions. Although the Bilingual Aphasia Test is meant to assess neurological disorders affecting language, we hypothesised that ceiling performance in L2 would be rare and L2 errors should be consistent with lack of processing automaticity. Initial data from 13 French-English and 4 English-French bilinguals confirm these expectations. Thus, the automated Syntactic Comprehension task (available online for PC and Mac platforms) is indeed suited to test bilingual English and French proficiency levels in healthy adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".