Do the cognacy characteristics of loanwords make them more easily learned than noncognates?
Bibliographic record
Abstract
This study investigates the effects of cognacy on vocabulary learning. The research expands on earlier designs by measuring learning of English–Japanese cognates with both decontextualized and contextualized tests, scoring responses at two levels of sensitivity, and examining learning in a more ecologically valid setting. The results indicated that Japanese learners could successfully recall the L2 forms of more cognates than noncognates, supporting earlier findings. However, when scoring was sensitive to partial knowledge of written form, the results indicated that greater knowledge of noncognates was gained. Because there was greater potential for learning noncognates due to the higher pretest scores for cognates, relative gains were also examined. The relative gains were greater for cognates than noncognates on a form recall test. The results of a cloze test contrasted with those of the form recall test. Gains were significantly larger for noncognates than cognates immediately after the treatment although no statistically significant difference existed 1 week after learning. Taken together, the research indicates that although the L2 forms of cognates may be more easily learned, it may be more challenging for second language learners to use cognates than noncognates, at least shortly after learning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".