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Record W3211668706 · doi:10.23977/aetp.2021.58013

The Development and Value of Blockchain Technology + Higher Education under the Background of China's Higher Education Reform and Innovation

2021· article· en· W3211668706 on OpenAlexvenueno aff
Zhaojun Guo, Ye Mei

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2021
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Technologies in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationInformatizationChinaBlockchainValue chainValue (mathematics)Economic growthAlliancePolitical scienceBusinessPublic relationsEconomicsMarketingEngineeringSupply chainComputer scienceComputer security

Abstract

fetched live from OpenAlex

With the popularization of higher education in China, promoting the development of higher education informatization is an important subject to realize the connotative development of higher education. In May 2018, dozens of colleges and universities across the country jointly established the blockchain alliance, named Youth Education Chain , initiated by x-lab of tsinghua university and announced the launch of Youth Education Chain . This is the first blockchain-based public education chain jointly built by universities in the world. By taking the establishment of Youth Education Chain platform as the entry point, this paper holds that the university enrollment based on Youth Education Chain will be of great value to the reform and innovation of China's higher education in the aspects of constructing the Shared education ecology, changing the traditional teaching mode and improving the efficiency of school management.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.007
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.343
Teacher spread0.325 · 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 designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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