J’ai l’impression que: Lexical Bundles in the Dialogues of Beginner French Textbooks
Bibliographic record
Abstract
Formulaic language is notoriously difficult for second language learners of French to master (Edmonds, 2014; Forsberg, 2010). Yet, no study has examined formulaic language in French textbooks despite the fact that in many contexts, textbooks represent a significant proportion of the input that learners receive. The current study addresses this gap. Using a distributional approach (as used in Biber, Conrad, & Cortes, 2004), four-word lexical bundles were extracted from an oral corpus of French. The average number of lexical bundles in oral corpus utterances was compared to the average number of bundles in a corpus of A1-B1 level textbook dialogues. An independent samples t test showed that the average number of lexical bundles per 100,000 words was significantly higher in texts from the oral corpus than the textbook corpus. The average number of stance and referential lexical bundles was also revealed to be higher in the oral corpus. Implications for textbook design are discussed, such as increasing the amount of formulaic language in A2 level textbooks and incorporating more authentic language into textbooks.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".