Evidence map›Paper›PMID 36052040›Full record

ArticleComputational intelligence and neuroscience2022

The Acceptability of Traditional Culture under the Background of Deep Learning.

Qian Sima, Shan Wu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Qian SimaSchool of Fine Art and Design, Kunming University, Kunming 650214, Yunnan, China.ORCID https://orcid.org/0000-0002-0119-1545
Shan WuSchool of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China.ORCID https://orcid.org/0000-0002-6418-8255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cultural values of a country impact its national psychology and identity. Citizens' values and public opinions are conveyed to state leaders over the media and other information channels, both directly and indirectly influencing decisions on foreign policy. The traditional cultural values that affect the psyche of the Chinese people are harmony, generosity, morality, courtesy, wisdom, honesty, loyalty, and filial piety. This study aims to analyze the attitudes of Chinese college students toward traditional culture. The reliability and effect factors of the traditional culture acceptability questionnaire are set in the context of deep learning. A questionnaire on traditional culture education of college students is compiled using the indicator evaluation methods, and the current situation of traditional culture education is investigated for college students. A total of 300 valid respondents are returned from five universities including Shanghai Jiaotong University, Fudan University, Yunnan University, Kunming University of Science and Technology, and Yunnan Normal University. Results show that 28% of college students believe that practical activities including visiting and learning and traditional festival commemoration are the most effective ways to educate traditional culture for them, which accounts for the largest percentage. Similarly, 19% of students suggest online publicity, while 16% believe that lecture reports are particularly important, and 12% of students advocate the teaching courses. In addition, about 23% of the students choose other methods, such as seminars, setting up Chinese culture festivals, and building cultural associations. The outcomes of this study provide data support for identifying the shortcomings in traditional cultural education and formulating strategies.

Indexed as

Deep LearningChinaHumansReproducibility of ResultsStudentsUniversities

Identifiers

PMID36052040
PMCPMC9427224

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.