Evidence map›Paper›PMID 35788864›Full record

ArticleJournal of psycholinguistic research2023

Emotive Tonality of the "Youth-Old Age" Dichotomy in Russian and Chinese Media Discourses: The Stage of Psychosemantic Expertise.

Meng Qin, Dongxia Xiao, Lin Yuan, Irina Karabulatova

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of psycholinguistic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 24% of its field
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, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Meng QinDepartment of Foreign Languages, Faculty of Philology, Peoples' Friendship, University of Russia, Moscow, Russian Federation.
Dongxia XiaoDepartment of Foreign Languages, Faculty of Philology, Peoples' Friendship, University of Russia, Moscow, Russian Federation.
Lin YuanDepartment of Foreign Languages, Faculty of Philology, Peoples' Friendship, University of Russia, Moscow, Russian Federation.
Irina KarabulatovaLaboratory of Machine Learning and Semantic Analysis, Institute of Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russian Federation. radogost2000@mail.ru.
Peoples' Friendship University of Russia · RUMoscow Institute of Physics and Technology · RU

Funding

Russian Academy of Sciences and the Krasnoyarsk Scientific Foundation 22-18-20109
6 · The paper itself

Abstract

The issue of attitude to age has the character of social stigma at different stages of human civilization, therefore, the identification of the general and particular in the assessments of the "youth-old age" dichotomy in different structural languages seems relevant. The results are based on the materials of an in-depth interview among 510 respondents belonging to different age groups: young, middle, and elderly; and 1257 respondents' responses. The in-depth interview covered 128 characteristics of interpersonal interaction. This made it possible to compile descriptor tables. The 54 characteristics identified were 27 bipolar scales. The objects of evaluation are characterized by 8 pole positions. A comparison was also made with data extracted from 368 articles in Russian and Chinese media. This work contributes to the understanding of the psycholinguistic processes of emotional perception of age in Russian and Chinese linguistic cultures.

Indexed as

EmotionsAdolescentAgedAge FactorsAgeismHumansInterpersonal RelationsLanguageMiddle AgedPsycholinguisticsClassification of emotionsOld ageSentiment analysisYouth

Identifiers

PMID35788864
OpenAlexW4283814131

What OpenQuestion holds

Textmetadata
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.