Exploring the Effects of First- and Second-Language Proficiency on Summarizing in French as a Second Language», in Canadian
Bibliographic record
Abstract
University students studying a second language are often required to sum-marize information they read or hear in that language. These learners bring with them a number of first-language summarization skills which may have an effect on how they acquire second-language summarization skills. What macrorules of summarization are actually affected by either first-language or second-language proficiency? According to the results of this study, both first-language summarizing skills and second-language proficiency affect second-language summarizing skills, except for inclusion of main ideas and amount of distortion which are more affected by first-language summarizing skills. Neither first-language summarizing skills nor second-language profi-ciency have an effect on combining within and across paragraphs and the use of macropropositions. Suggestions for teaching and future research conclude the paper. Les étudiants de niveau universitaire qui étudient une langue seconde ont sou-vent a ̀ résumer l’information qu’ils lisent ou entendent dans cette langue. Ces apprenants font preuve d’un nombre d’habiletés propres au résume ́ de texte dans leur langue maternelle qui pourraient avoir une influence sur la façon d’acquérir ces habiletés dans une langue seconde.Quellesmacrorègles propres au résume ́ de texte sont en fait influencées soit par la compétence en langue maternelle soit par la compétence en langue seconde? Selon les résultats de cette étude, ces deux facteurs ont une influence sur l’habilete ́ a ̀ résumer un texte dans la langue seconde, exception faite toutefois de l’incorporation des idées principales et du degre ́ de distorsion du message qui sont plus influ-encés par leur habilete ́ a ̀ résumer un texte dans leur langue maternelle. Ni la compétence dans ce domaine en langue maternelle, ni la compétence en langue seconde n’exercent une influence sur la combinaison de phrases au sein d’un paragraphe ou de plusieurs paragraphes, ou encore sur l’utilisation de macropropositions. Des suggestions quant a ̀ l’enseignement de ces habiletés et quant aux recherches a ̀ venir concluent cette étude.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".