Certaines considérations sur les particularités psycholinguistiques de la traduction et de l'autotraduction comme processus de médiation culturelle
Bibliographic record
Abstract
In this article, we examine the psycholinguistic specificities of translation and self-translation as processes of cultural mediation in multilingual spaces. This strategy employs a multidisciplinary approach (experimental psycholinguistics, comparative studies, discourse analysis) and focuses primarily on the Republic of Moldova, where Romanian (79.9%), Russian (11.6%), Gagauz (3.6%), Ukrainian (3.0%), Bulgarian (1.2%), Romani (0.3%), and other languages coexist. We compare all the results presented here with those relating to other multilingual societies (those of Canada, Belgium, etc.). Validated data (censuses, empirical studies) are cited to support the analysis. The article references the research of Grosjean, Jeanneret, Pavlenko, Kroll, Bialystok, Dewaele, and other scientists. Two tables summarize the sociolinguistic data and the comparative cognitive performance. One figure schematically represents the linguistic distribution in Moldova, and another illustrates the brain mechanisms of bilingualism. Concrete examples (bilingual writers, educational policies) are presented. The analysis shows that translation and self-translation engage cognitive mechanisms of inhibition and control specific to bilinguals, shaping complex identity postures. These linguistic processes are strongly contextualized by the sociocultural situation (language status, language policies, interethnic contacts). In conclusion, the importance of considering these psycholinguistic dimensions in language training, cultural mediation, and understanding identity dynamics in bilingual communities is emphasized.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".