The Effects of Giving and Receiving Marginal L1 Glosses on L2 Vocabulary Learning by Upper Secondary Learners
Bibliographic record
Abstract
<p>This paper reports the findings of a study that investigated the effect of giving and receiving marginal L1 glosses on L2 vocabulary learning. To that end, forty nine Iranian learners of English were assigned to three different experimental conditions including marginal L1 glosses Giver (n = 17), marginal L1 glosses Receiver (n = 17), and no glosses Control group (n = 15) with a pretest, immediate, and delayed posttests design. The scores obtained from the fill-in-the-blank and translation test confirmed the homogeneity of the three participating groups in the pretest. During three treatment sessions, participants in the giver group were required to perform the three reading comprehension tasks and consult the bilingual dictionary to look up the targeted lexical items, which were highlighted, and write down their L1 equivalents in the spaces given. The participants in the receiver group were asked to carry out the same reading comprehension tasks which included L1 equivalents of the targeted words. The participants in no marginal glosses group took the same procedure while they had no access to marginal glosses. Two days and four weeks after treatment sessions, all participants took the posttests using the same testing package applied in the pretest. Results of one-way ANOVAs revealed that both the giver and receiver group had an influence on L2 vocabulary learning, the giver group made the most favorable progress over time.</p>
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".