‘It's vocabulary’/‘it's gender’: learner awareness and incidental learning
Bibliographic record
Abstract
Research has shown that second language (L2) learners that become aware of linguistic features during grammar-based tasks are better able to process these features on a posttest compared to learners that do not focus on these features. However, much L2 input does not come in the form of grammar-based tasks. This study investigates whether learners who become aware of French grammatical gender during a meaning-based task are better able to process these forms than learners whose experience with the same task does not lead to awareness of the feature. Thirty-six Anglophones with low-level French were exposed to reliable noun-ending clues to grammatical gender whilst completing a crossword task. A think-aloud protocol and two probe questions measured awareness. A pre- to posttest design measured accuracy with French nouns ending in eau (e.g. le cadeau). The results revealed no advantage for learners who became aware of the noun-ending clues during exposure: all learners improved in their ability to judge the gender of words encountered during the task (item learning) but none were able to extend this new knowledge to novel items (system learning). The interpretation of the findings considers the choice of linguistic feature, the role of awareness in item learning and the learning conditions that might be necessary for awareness of form to occur during meaning-based exposure.
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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 | 0.008 |
| 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 | 0.001 |
| 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".