Use of the Semantic Differential in Bilingualism Research and its Relevance to Translation
Bibliographic record
Abstract
To investigate bilingual subjects' perceptions of the connotative differences between concepts in English and French, a form of the semantic differential was employed in which the scales were derived from Cattell's 16 personality factors. Altogether 16 concepts were rated and these were made up of four sets, each set containing a pair of synonyms in English and a pair of synonyms (their translation-equivalents) in French. Even though the sets themselves were easily distinguishable in terms of their affective meaning, no significant differences in affective meaning emerged between the concepts in any of the sets either within or across languages. There were, however, significant differences between individuals in the ways they perceived the concepts. Some of these differences seemed to be due to the effects of dominant language, A cluster analysis of the individuals in terms of the semantic difference between concepts and their translation-equivalents (over and above the difference between synonyms) gave little support to the postulated distinction between the two types of bilingual, compound and coordinate, although there was some evidence that the compound bilingual exists as a separate type.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".