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Record W2375918777

Who Knows More about the Internet:University Students in Vancouver or Shanghai?

2007· article· en· W2375918777 on OpenAlexaboutno aff
Huang Yan

Bibliographic record

VenueComparative Education Review · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetPraiseEntertainmentSociologyPragmatismGospelMedia studiesPolitical sciencePsychologyHistoryLawComputer scienceWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

The Internet is deemed to be an essential tool for education,entertainment and daily life.Vancouver and Shanghai are both coastal cities with ambiguous relations to the centre.Each has key universities involved in teacher training with historic commitments to adult education.Both are at the forefront of Internet developments and university leaders praise the Internet and preach knowledge economy gospel.But what do Vancouver and Shanghai university students know about the Internet? Who knows more? This question was investigated by administering the Internet Quiz to 516 university students in Shanghai and 566 in Greater Vancouver.Students in Greater Vancouver completed the English and Shanghai students the Chinese version of the Internet Quiz.Vancouver students knew 20% more about the Internet than those in Shanghai and,as well,were using it for different purposes.The authors also make suggestions for change,explore aspects of Chinese student pragmatism.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.080
GPT teacher head0.398
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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