A Localization Theory: User Experience Research in the United States & Canada
Bibliographic record
Abstract
abstract: Today, in the internet-age with global communication every day, it is more important than ever to learn how best to communicate across cultures. However, a review of literature and localization research reveals no studies comparing written communication preferences between cultures using the English language. This gap in research led me to my question–How do localization needs or preferences differ between English-speakers in the U.S. and Canada? To answer my research question, I created a study focused on written communication using a quality measure after consulting the IBM rubric (Hofstede, 1984). I incorporated a demographics questionnaire, a sample document of an Alberta Government brochure, and a survey to measure participant perceptions of quality for use with the sample document. Participants for the study were recruited from Phoenix, Arizona and Edmonton, Alberta, Canada. All participants reviewed the Canada-based sample document and answered the questions from the survey. The survey responses were designed to obtain data on culturally specific variables on contexting, which were critical in understanding cultural differences and communication preferences between the two groups. Results of the data analysis indicate differences in cultural preferences specific to language, the amount of text, and document organization. The results suggest that there may be more significant differences than previously assumed (Hall, 1976) between U.S. and Canadian English-speaking populations. Further research could include a similar study using a U.S.–based document and administering it to the same target population. Additionally, a quality-based measure could be applied as a way of understanding other cultures for localization needs, since inadequate localization can have an adverse impact on perceptions of quality.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".