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Record W2605997610 · doi:10.7577/seminar.2350

Website Preferences of Finnish and Mexican University Students: A Cross-Cultural Study

2015· article· en· W2605997610 on OpenAlexaboutno aff
Miguel Santiago, Pirkko Hyvönen

Bibliographic record

VenueSeminar net · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersUniversidad Autónoma de Nayarit
KeywordsThe InternetQuarter (Canadian coin)MulticulturalismPsychologyData collectionMedical educationWorld Wide WebPedagogySociologyComputer scienceGeographyMedicineSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.373
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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