Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment

SSRN Electronic Journal ◽

10.2139/ssrn.3763856 ◽

2021 ◽

Author(s):

Ian Burn ◽

Daniel Firoozi ◽

Daniel Ladd ◽

David Neumark

Keyword(s):

Machine Learning ◽

Age Stereotypes ◽

Perceived Age ◽

Job Ads

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Understanding the mechanisms underlying the effects of negative age stereotypes and perceived age discrimination on older adults’ well-being

Aging & Mental Health ◽

10.1080/13607863.2018.1514487 ◽

2018 ◽

Vol 23 (12) ◽

pp. 1666-1673 ◽

Cited By ~ 4

Author(s):

Manon Marquet ◽

Alison L. Chasteen ◽

Jason E. Plaks ◽

Laksmiina Balasubramaniam

Keyword(s):

Older Adults ◽

Well Being ◽

Age Discrimination ◽

Age Stereotypes ◽

Perceived Age

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When is age dissimilarity harmful for organisational identification? The moderating role of age stereotypes and perceived age-related treatment

Human Relations ◽

10.1177/0018726719900009 ◽

2020 ◽

pp. 001872671990000

Author(s):

Alessia Sammarra ◽

Silvia Profili ◽

Riccardo Peccei ◽

Laura Innocenti

Keyword(s):

Age Group ◽

Age Stereotypes ◽

Age Diversity ◽

Age Related ◽

The Social ◽

Perceived Age ◽

The Relationship ◽

Potential Challenge ◽

Social Identity Approach

Due to demographic changes, age diversity is growing in the workplace, creating a potential challenge to social integration. However, who is most affected by working with colleagues of different ages and when is being dissimilar in age from others more likely to hinder organisational identification? Drawing on relational demography and on the social identity approach, we suggest that certain individual and contextual conditions can lead employees to react to greater age dissimilarity by reducing their psychological attachment to the organisation. We propose that negative age stereotypes and perceived age-related treatment affect the salience of age as a social category for employees and threaten their age group identity, thereby creating conditions in which age dissimilarity might hinder organisational identification. We therefore examine the moderating effects of negative age stereotypes and perceived age-related treatment on the relationship between age dissimilarity and organisational identification in a sample of 434 schoolteachers from 16 schools in Italy. Findings show that age dissimilarity per se is not sufficient to hamper employees’ identification with the organisation. However, it has detrimental effects when employees hold negative age stereotypes and/or perceive an unfair organisational treatment towards their own age group. Implications for research are discussed along with practice implications.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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