Testing the Triggering Hypothesis: Effect of Cognate Status on Code-Switching and Disfluencies
Bibliographic record
Abstract
"Triggered switching" is the theory that code-switching happens more often with words connected to both languages, such as cognates. Corpus analyses have supported this theory; however, they do not allow testing for directional causality. Here, we test the triggering hypothesis through a picture-naming task, and examine whether cognates trigger code-switches, as well as more subtle interference effects resulting in disfluencies. Forty English-Spanish bilinguals completed a picture-cued sentence production task in three conditions: English-only, Spanish-only, and mixed. Half of the pictures represented Spanish-English cognates. Unsurprisingly, participants were more likely to code-switch when asked to use both their languages compared to only their dominant or non-dominant language. However, participants were not more likely to switch languages for cognate than for non-cognate trials. Participants tended to be more fluent on cognate trials in the dominant and the non-dominant condition, and on non-cognate trials in the mixed-language condition, although these effects were not significant. These findings suggest that both language context and cognate status are important to consider when testing both overt switches and disfluencies in bilingual speech production.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".