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Record W1920739248

Applying WeChat Platform to Collaboratively Promote College Students to Read Classics and Improve Their Humanistic and Cultural Literacy

2015· article· en· W1920739248 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHigher education of social science · 2015
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)HumanismInformation literacyComputer scienceLiteracySociologyMathematics educationMultimediaWorld Wide WebPedagogyPsychologyPolitical scienceLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

In 2011, the advent of WeChat has had a great influence on all areas. Our library is also actively exploring related WeChat-based services. This article will embed classics into WeChat platform, using rich literature resources from the library and applying WeChat as an information dissemination channel to realize the push of classic books, classic articles or fragments, quick search link of library literature, rapid dissemination of information on reading activities, so that reading can be achieved anywhere, anytime to promote college student to read classics, guide college students to love reading classics and thereby collaboratively improve college students’ humanistic and cultural literacy.

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.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.056
GPT teacher head0.408
Teacher spread0.351 · 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