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Getting warm(er) an investigation into linguistic relativity and its significance in the translation of the English lexical term "warm" into French

2008· dissertation· en· W2767253413 on OpenAlex
E.R. Addison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsVocabularyVariety (cybernetics)Affect (linguistics)Meaning (existential)Lexical itemLiteral translationTerm (time)PsychologyLiteral and figurative languageLexical densityComputer scienceSource textArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.313
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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