Using eye tracking to investigate what bilinguals notice about linguistic landscape images
Bibliographic record
Abstract
Abstract In daily life, we experience dynamic visual input referred to as the “linguistic landscape” (LL), comprised of images and text, for example, signs, and billboards ( Gorter, 2013 ; Landry & Bourhis, 1997 ; Shohamy, Ben-Rafael and Barni 2010). While much is known about LLs descriptively, less is known about what people notice when viewing LLs. Building upon the bilingual eye movement reading literature (e.g., Whitford, Pivneva, & Titone, 2016 ) and the scene viewing literature (e.g., Henderson & Ferreira, 2004 ), we report a preliminary study of French-English bilinguals’ eye movements as they viewed LL images from Montréal. These preliminary data suggest that eye tracking is a promising new method for investigating how people with different language backgrounds process real-world LL images.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".