Attending to second language lexical stress: exploring the roles of metalinguistic awareness and self-assessment
Bibliographic record
Abstract
This study examines the relationship between German second language (L2) learners’ awareness of the German lexical stress assignment system and their ability to accurately assign stress to cognate words with predictable lexical stress. Participants were 31 adult L2 German learners from three groups: native English speakers with a range of German proficiency levels (N = 10), native French speakers with intermediate German proficiency (N = 10), and native French speakers with advanced German proficiency (N = 11). They produced target items in a carrier phrase and then indicated both which syllable they stressed and where stress is supposed to fall. Finally, they provided a rule for assigning stress to each word. Stress production accuracy was similar across the groups, regardless of L1 or L2 proficiency. Participants’ ability to verbalize where they had placed stress was a significant predictor of stress assignment accuracy. They produced relatively few rules overall, and the rules they produced were mostly inaccurate. The results point to the greater importance of self-assessment over the ability to produce discrete rules in accurate lexical stress assignment.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| 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".