The Meanings of Pronominal-Verbal Constructions for Speakers and Learners of French
Bibliographic record
Abstract
This article reports on a study of the interpretations of French pronominal-verbal constructions, and on the classification of those interpretations as 'reflexive', 'reciprocal', 'intrinsic', or 'passive'. Nineteen Francophone and 19 non-Francophone students in university degree programmes in English French translation interpreted 20 sentences with pronominal-verbal constructions having, out of context, one or more of the four possible readings. To do this, they wrote a translation or a paraphrase corresponding to each reading. They also identified each of the readings which they recognized as reflexive, reciprocal, intrinsic, or passive, having been given a written and oral explanation of these interpretation types. The results of the study showed greater correctness in rendering and identifying reflexive and reciprocal readings, on the one hand, than for intrinsic and passive readings, on the other. One major source of difficulty was the metalinguistic aspect of the task: there was a great tendency to misclassify correct non-reflexive interpretations as reflexive. Another was the preference among Francophones for paraphrase over translation as the means to express an interpretation. This posed the greatest problem in the case of the intrinsic, which must be interpreted by a verb lexically different from that in the reflexive, reciprocal, and/or passive reading. This requirement was best met by translating, not paraphrasing, the specifically intrinsic reading, since within one language, there are no perfect synonyms.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".