Cognate awareness -raising in late childhood: teachable and useful
Bibliographic record
Abstract
This study is part of a larger investigation of the usefulness of instruction designed to raise cross-linguistic awareness in young Francophone learners of English in Quebec. In the research reported here, the focus is on cognates. Since previous research shows that learners typically fail to recognise many helpful similarities between words in a new language (in this case, English) and languages they already know, the instructional activities we designed emphasised strategies for identifying ‘good friend’ resemblances, though false friends were also discussed. The impact of the activities was assessed in three ways: learners’ performance on a measure of French–English cognate recognition ability; their written responses to a question that probed developing cognate awareness; and interviews that explored teachers’ experiences after using the activities in their classes. Findings suggested that learners benefited from the activities. Gains on the recognition test pointed to an advantage for ‘pattern’ instruction that addresses resemblances that are not readily detected (e.g. English screen = French écran). Learners who received the experimental instruction outperformed control groups on the cognate awareness measure. Furthermore, teachers were positive about the cross-linguistic comparisons. We conclude that the activities were effective and even enjoyable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".