Microanalysis of lexical depth in a second language: the implicit and the expressible aspects of the definitional skill
Bibliographic record
Abstract
The research found that, in terms of implicit deep word knowledge, the heritage language group, the content-based group, and the monolingual group performed similarly in English on the Word Association Test. In terms of English expressible deep word knowledge, the heritage language group and the monolingual group performed similarly and, in some cases, better than the content-based group on the English Word Definition Task. For Farsi expressible deep word knowledge, there was no difference between the content-based group and the heritage language group on the Farsi Word Definition Task. This research compares three groups of elementary-school children in Toronto by testing their deep lexical knowledge of specific words (implicit aspect) and grading their expression of such knowledge in a standard definition form (expressible aspect). This research concludes that for both aspects of lexical knowledge in children, length of residence in the L2 context and instruction in L2 may be more important than L1 literacy per se. This suggests that L1 literacy in a two-way bilingual education environment, and in combination with simultaneous L2 academic instruction, might have a stronger impact than mere L1 literacy on the development of academic skills and definitional knowledge. The research showed the important role of curricular L2 exposure (or language of instruction) in the development of students' deep lexical knowledge, and the weaker role of L1 academic skill, be it lack of knowledge or transfer, in providing definitions in the L2. In other words, although the findings demonstrate that longer length of residence in an L2 context and L2 schooling correlates with better performance on the implicit and expressible measures of L2 lexical depth, longer residence in Iran with some L1 content-based schooling does not correlate with higher definitional quality in Farsi. To compare the children's lexical depth, all were tested on a Word Association Test (in English) and a Word Definition Task (in English and Farsi.) Their degree of expressible knowledge was derived from the quality of the definitions produced which were analyzed in terms of linguistic and semantic content. Child participants were drawn from three groups of fifth and sixth graders: one monolingual group (English L1) and two bilingual groups (Farsi L1, English L2). These last were immigrant children with some formal content-based schooling in L1 (the content-based group) and immigrant children with basic L1 literacy through Toronto heritage-language schools (the heritage language group).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".