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Record W2394934098

The Impact of Micro Media Communication on the Effectiveness of Ideological and Political Education and Its Countermeasures

2016· article· en· W2394934098 on OpenAlexvenueno aff
Xu Qin

Bibliographic record

VenueHigher education of social science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyVirtuality (gaming)Political communicationPoliticsNew mediaSWORDNormalization (sociology)Political educationSociologyPublic relationsPolitical scienceSocial scienceComputer scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Micro media communication is a double-edged sword, and it will produce some impacts on the effectiveness of ideological and political education showing in the following aspects: the decentration of the way of micro media communication weakens the discourse power of ideological and political education; the instantaneity of communication weakens the information superiority of ideological and political education; the extensiveness of the micro people(people who use the micro media communication) cuts down the pertinence of ideological and political education; the complexity of communication content reduces the impact of the ideological and political education content; the virtuality of feedback reduces the effect of the ideological and political education. According to these challenges, the development of ideological and political education should make the micro media platform “learning tour ” normalization, enhance the ability to set the issue , dig deeper to the frontier theory behind micro media hot topics, and make effective teaching combined with the traditional carrier.

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)0.006
metaresearch head score (Gemma)0.030
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.034
GPT teacher head0.394
Teacher spread0.361 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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