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Record W2923493276 · doi:10.3968/10762

The Background and Value of the Research on Sense of Social Responsibility in China

2018· article· en· W2923493276 on OpenAlexvenueno aff
Chun Su

Bibliographic record

VenueCross-cultural communication · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSocial responsibilityValue (mathematics)Environmental ethicsCivilizationChinaSociologyPublic relationsSocial philosophyCorporate social responsibilityMoral responsibilityPolitical scienceSocial psychologySocial sciencePsychologySocial relationLaw

Abstract

fetched live from OpenAlex

As a moral quality carries social and individual value in the contemporary society, sense of social responsibility is not only excellent civilization of the Chinese nation but also a fundamental moral quality that individuals are supposed to possess in the new era. In recent years, however, individuals’ sense of social responsibility has faced severe challenges from mutiple aspects, that is to say, failing to fulfill their social responsibilities, lacking sense of social responsibility under the central national governance and confronting social responsibility risks generated by the challenges of public crisis. Therefore, in-depth systematic thinking on the research of individual sense of social responsibility and theoretical guidance on cultivation of sense of social responsibility will help deepen people’s understanding of the sense of social responsibility and enrich the theory of cultivating the sense of social responsibility. Based on the above-mentioned aspects, the problems of individual sense of social responsibility will be better revealed and the level of cultivating the quality can be improved.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0030.005
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.437
Teacher spread0.354 · 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 designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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