Bibliographic record
Abstract
Some previous research has suggested that words in multlinguals’ first language, particularly taboo words, evoke a greater emotional response than words in any subsequent language. In the present study, we elicited French-English bilinguals’ emotional responses to words in both languages. We expected taboo words to evoke higher emotional response than positive or negative words in both languages. We tested the hypothesis that the earlier that bilinguals had acquired the language, the higher the emotional responses. French-English bilinguals with long exposure to both French and English participated. Their galvanic skin response (GSR) was measured as they processed positive (e.g., mother), negative (e.g., war) and taboo (e.g., pussy) words in both French and English. As predicted, GSR responses to taboo words were high in both languages. Surprisingly, English taboo words elicited higher GSR responses than French ones and age of acquisition was not related to GSR. We argue that these results are related to the context in which this study took place (i.e., an English majority context). If this interpretation is correct, then bilinguals’ emotional response to words could be more strongly linked to recent emotional interactions than to childhood experiences.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.212 | 0.029 |
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".