Do the cognacy characteristics of loanwords make them more easily learned than noncognates?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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 it