Website Preferences of Finnish and Mexican University Students: A Cross-Cultural Study
Bibliographic record
Abstract
This paper is focused on understanding Internet use and comparingcross-cultural differencesaccording tothe contents and preferences of the websites that are most visited bytwogroupsof university students from Finland (n=30) and Mexico (n=30).The following research is anexploratory qualitative study with some basic statistics. A questionnairewas used in this study as a data collection instrument. The findings show that in both groups, university students prefer websites about social networking (Facebook), sending email (MSN), videos (YouTube), multiplatform applications (Google), educational sites (UniversityofOulu), and wikis (Wikipedia). Thisdemonstratedthat both groups have an interest in sharing ideas and meetingfriends.The differences reveal that Finnish students use their university’swebsite more regularly thanthe Mexican student respondents and that theytend to implementtheirideas more often.Furthermore, thisstudyexplored how university students use the Internet and whattype of influencethe Internet has onthem.The emotional effects suggest thatalmost quarter ofstudents reportedusing the internet to escapenegativefeelings, such as depression or nervousness.The findings provide information for university teachers about students’habitsand prior knowledge regarding Internetusefor educational purposes. The informationwill behelpful when designing learning and teaching in multicultural student groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".