How language affects consumers' processing of numerical cues
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.
Bibliographic record
Abstract
Abstract We show that linguistic numeral structures affect consumers' comparative evaluations of numbers, prices, and alphanumeric brand names. For example, 80 (eighty) in English is perceived as 4 × 20 ( quatre‐vingts or four twenties) in French and as 8 × 10 ( ba‐shi or eight tens) in Chinese. Thus, the difference between 80 and 20 is expressed with different degrees of numerosity, the number of units into which a stimulus is divided: (a) 2 × 10 versus 8 × 10 in Chinese, (b) 20 versus 4 × 20 in French, or (c) simply 20 versus 80 in English. In four studies involving a total of 732 bilinguals who speak two of these three languages, we examine how different linguistic properties can lead to differences in comparison of numerical values and inferences made about product attributes. We demonstrate the mediating role of numerosity induced by certain linguistic structures while ruling out alternative explanations for this phenomenon such as cultural differences, processing fluency, and numeracy. Our research contributes to literatures on number cognition, numerosity, branding, and linguistics while providing insights for international marketers by encouraging practitioners to use different numbers in their marketing, branding, and pricing efforts in ways that best fit the linguistic structure of the country in which they sell a product.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it