Getting warm(er) an investigation into linguistic relativity and its significance in the translation of the English lexical term "warm" into French
Bibliographic record
Abstract
Students of foreign languages are well aware that every language has its own vocabulary and word-for-word translations are rarely valid. It is therefore unsurprising that identifying literal translations in French for the English lexical term “warm” is problematic. This study demonstrates that not only is there a variety of French lexical terms that can be used to convey the meaning that the English lexical term “warm” conveys, but that certain French lexical terms are more likely to be used only in certain situations. Furthermore, an examination of this phenomenon through the lens of linguistic relativity has revealed differing conceptualizations of temperature for native French versus native English speakers. Linguistic relativity is the theory that one’s native language can actually affect the way one thinks about the world. In this study, the theory is examined from the points of view of various linguists and translators, including Whorf, Saussure, Wierzbicka, and others. Linguistic relativity is then applied to French and English speakers’ conceptualizations of temperature. Both oral and written data is collected for this study; participants are both interviewed on tape and fill out a written questionnaire. Native French speakers are from various regions of France, Switzerland, Quebec, Africa, and South Louisiana. This study is limited to the adjectival and non-figurative use of the English lexical term “warm”. The results of this study reveal that while there are many possible translation into French of the English lexical term “warm” depending on the situation and the speakers’ personal preferences and intents, certain French lexical terms are more likely to be used in particular situations. Based on the results of this study, the preferred French translations of the English lexical term “warm” are: chaud ‘hot’, tiède ‘lukewarm’, and bon ‘good’. Due to their differing language systems, native French speakers and native English speakers classify temperatures differently, and in doing so, their experiences of temperature are interpreted differently. This difference in interpretation undoubtedly means that linguistic relativity is at play.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".