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. 2025 Dec 31;9(Suppl 2):igaf122.3699. doi: 10.1093/geroni/igaf122.3699

Age Bias in Employment-Related Descriptions: A Word Embedding Analysis of People’s Daily

Chunyan Mai 1, Yue Hu 2
PMCID: PMC12761996

Abstract

Background

The population is aging globally and older adults’ labor participation is rising. It’s critical to explore factors for post-retirement reemployment. Yet research on the role of aging attitudes is rare. This study investigated age biases in employment-related descriptions in domains of health, personality, skills, employment in People’s Daily from year 1950 to 2021 by exploring whether older age groups are framed as more positively or negatively compared to younger cohorts amid China’s socioeconomic transitions.

Methods

Using natural language processing, word embedding with cosine similarity was applied to analyze associations between age-related words and descriptive words on these people. It was hypothesized that there were periodic fluctuations in age bias across historical stages and stronger associations of younger groups with employment-related words.

Results

Key findings included persistent youth-centric bias, especially post-1980s; fluctuating bias (balanced 1950s–1970s, youth-tilted 1980s–2000s, partially corrected post-2010s); and dimensional differences–health tied to medical resources/aging demands, stable personality perceptions, volatile skills due to industrial shifts, and persistent negative employment bias toward older age groups.

Discussion

Trends reflect adaptations to economic/industrial changes, with societal reliance on youth vitality as a common thread. Differences across domains stem from alignments with age-based values (experience/vitality), illustrating dynamic workplace value adjustments during China’s transitions.


Articles from Innovation in Aging are provided here courtesy of Oxford University Press

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