Concreteness and Language Effects in the Quality of Written Definitions in L1, L2 and L3
Bibliographic record
Abstract
Several researchers have suggested that definitional skill explains academic success/failure (Gagné, 2004; Snow, 1987). The words used to investigate definitional skill have all been concrete words given in the first language (L1) and/or the second language (L2) of the participants. This paper reports a study investigating the quality of the definitions of concrete nouns and of abstract nouns in three languages: Arabic (L1), French (L2) and English (L3). Two hundred and thirty students (mean age: 17.1) wrote definitions for five concrete nouns and five abstract nouns in each of their three languages. We examined the concreteness and language effects in the length and quality of definitions. Results showed that while in L1 there was no difference between the length of definitions of concrete nouns and those of abstract nouns, there was a difference in L2 and in L3. As for the definitional quality, there were significant differences between the definitional quality of concrete nouns and of abstract nouns in the three languages.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".