Language profiles of poor comprehenders in English and French
Bibliographic record
Abstract
This study explored components of language comprehension (vocabulary, grammar, and higher‐level language) skills for poor comprehenders in French immersion. We identified three groups of bilingual comprehenders (poor, average, and good) based on English reading performance and compared their language comprehension skills in English L1 and French L2. We also identified and compared English skills for three groups of monolingual comprehenders from English‐stream programmes. Among both bilingual and monolingual learners, poor comprehenders performed significantly lower than good comprehenders on English vocabulary, morphological awareness, and inference. Bilingual poor comprehenders also differed from average comprehenders on English morphological awareness and inference. Similar results were found in French for the bilingual learners. Lower scores on French vocabulary and morphological awareness distinguished between bilingual poor and good comprehenders. Additionally, weaknesses in French semantics and inference distinguished between bilingual poor and good comprehenders and bilingual poor and average comprehenders. These results suggest that poor comprehenders share remarkably similar language characteristics in L1 and L2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".