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Record W3108625625 · doi:10.5539/mas.v14n12p34

The Interaction Design Strategy of Foreign Exchange Students on Network Field

2020· article· en· W3108625625 on OpenAlexvenueno aff
dan SHANG, Yuan Xiang, yongxin GUAN

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Construct (python library)Adaptation (eye)Subject (documents)Computer scienceCultural exchangeEvent (particle physics)Public relationsSociologyKnowledge managementPsychologyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

In the background of the Belt and Road initiative, international exchanges and cooperation are increasingly frequent and the number of students studying abroad is on the rise. Foreign exchange students living abroad are easily influenced by the environment, field culture, discourse subject etc, so that it's common for their ideas to be impacted. Cultural adaptation and avoiding western cultural osmosis are extraordinarily important in the management of foreign exchange students. Field theory provides a new means to resolve this problem. By using questionnaire to collect data and through behavior event interview, understand the behavior of foreign exchange students in the use of new media. And then, design user portrait according to the data results and interview content and construct the field environment elements of online education.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.172
GPT teacher head0.419
Teacher spread0.247 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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