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Record W2237821340 · doi:10.5539/ass.v12n2p151

Descriptive Analysis Regarding Use of Wechat among University Students in China

2016· article· en· W2237821340 on OpenAlexvenueno aff
Xing Yu Zhu

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingChinaDescriptive statisticsSocial mediaPsychologyPhoneDescriptive researchMobile phoneMedical educationAdvertisingBusinessSociologyComputer sciencePolitical scienceWorld Wide WebMedicineSocial scienceStatistics

Abstract

fetched live from OpenAlex

<p>The main purpose this study is to explore that how the international students use Wechat in China and what kind of functions and social networking apply on Wechat. Furthermore the study indicated regarding what was most important opinion and information students adopt on Wechat. This research study is descriptive analysis about usage and Wechat as source of communication and contact with family, friends and teacher is highlighted in the paper. Total 200 international students respondents randomly were selected for data collection from Tsinghua University Beijing, China the 65.5% male and 34.5% of the female participated in the study the main findings of the study 94.5% of the respondents use Wechat to contact with friends and main purpose of use Wechat is the result showed 97.5% send message their friends and teachers. However, the 44% of the respondents agree the information that people tag and write on Wechat could be reliable. Furthermore, there many functions are available on Wechat where people can get benefit like call the taxi, transfer money recharge money in mobile phone and games but most of the respondents did not use it properly due to lack of Chinese language bearer among international students. Therefore it is possible that Wechat can introduce some new Apps where international students can get more benefit from it.</p>

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)0.002
metaresearch head score (Gemma)0.002
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 score0.181
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.280
Teacher spread0.261 · 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".

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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