Merits — Journal of Human Resources

Teachers’ emotional resilience is increasingly recognized as a target for counseling-informed workplace interventions that support sustainable professional functioning in demanding educational environments. Grounded in Ellen Langer’s socio-cognitive theory, this study examined the association between teachers’ mindfulness profiles and their use of emotion regulation strategies, highlighting implications for counseling and professional support. Socio-cognitive mindfulness conceptualizes mindfulness as active engagement, novelty seeking, and novelty producing, qualities closely aligned with reflective and adaptive processes fostered in counseling contexts. A sample of 402 in-service teachers completed self-report measures assessing mindfulness and emotion regulation. Latent Profile Analysis identified four distinct mindfulness profiles: Moderate–Stable, High Mindfulness, Novelty-Oriented with Lower Engagement, and High Engagement–Seeking with Lower Novelty Producing. Multivariate analyses revealed significant, though small, differences in emotion regulation across profiles. Teachers in the High Mindfulness profile reported greater use of active modification than those in the Moderate–Stable profile and lower avoidance than those in the High Engagement–Seeking with Lower Novelty Producing profile. Overall, findings partially supported the hypotheses, suggesting that certain socio-cognitive mindfulness profiles are associated with differences in emotion regulation strategies. These findings may inform future research on counseling-informed approaches to teacher professional development. The cross-sectional design and small effects do not establish causal mechanisms or the effectiveness of profile-specific interventions. Resilience and well-being were not directly assessed.

Artificial intelligence is commonly presented as neutral, autonomous, and future-oriented, yet its interfaces, narratives, and labor infrastructures remain shaped by older gendered expectations. This article advances a new account of AI feminization by connecting symbolic representation to the material organization of hidden labor. Using a mixed-methods design, the study combines interface analysis of fifteen widely used AI systems, critical discourse analysis of 500 public and industry artifacts published between 2018 and 2026, and critical infrastructural document analysis of job advertisements, contractor records, technical reports, model cards, labor documents, and workplace policies. The findings show that AI systems repeatedly perform feminized qualities such as care, patience, emotional responsiveness, availability, and service, while the labor producing these qualities is distributed across annotators, moderators, evaluators, and quality-assurance workers whose contributions remain obscured. The study further demonstrates that AI’s promises of empowerment and efficiency coexist with contractual precarity, surveillance, emotional burden, and unequal global value extraction. By theorizing AI as feminized in interface, discourse, and infrastructure, the article reveals algorithmic intelligence as a social relation rather than a technical achievement. More equitable AI futures therefore require labor visibility, accountability, worker protection, and infrastructural justice.

This study examines how interconnected artificial intelligence (AI) tools can turn early human resource (HR) allocations into evidence for later career decisions. A counterfactual-twin Monte Carlo simulation compares isolated and linked systems, alongside periodic record resets and opportunity equalization. Paired employees share fixed capability and random shocks but receive slightly different initial scores. Linking decisions amplifies this initial difference through opportunity allocation, observed performance and reusable records. Periodic resets reduce final-career sensitivity, while opportunity equalization affects access more strongly than terminal outcomes. Near parity in average group outcomes can coexist with positive sensitivity among matched individuals. Additional tests preserve the direction of the architecture and intervention contrasts across the examined coefficient settings, although their magnitudes vary. The findings motivate evaluating decision lineage, opportunity access and record reuse across HR systems. They identify mechanisms within a synthetic, uncalibrated model and do not estimate effects in real organizations.