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Facing the Fallout: How Technological Advancements and Epidemics Impact Workers’ Economic Stressors

2025· article· en· W4416002066 on OpenAlex
Andrea Bazzoli, Lara C. Roll, Lixin Jiang, Gwendolyn Paige Watson, Rebecca J. Lindgren

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsWatsonUnemploymentInvestment (military)CasualHarassmentAttributionDisengagement theoryBeijing

Abstract

fetched live from OpenAlex

This presenter symposium features five papers that examine how technology-related advancements and epidemics (e.g., opioid use) impact workers' economic stressors (e.g., job insecurity, occupation insecurity, and financial stress), as well as broader societal outcomes. Throughout the symposium, we will be focusing on disseminating cutting edge research related to these themes and focus on what organizations can do to alleviate adverse outcomes for workers. The presentations are as follow: - Attribution matters for the relationship between technological investment and job insecurity by Jiang et al. - Unpacking Occupation Insecurity: A Person-Centered Analysis of Educators' Perceptions and Well-Being in the Digital Era by Roll et al. - Under Economic Pressure: A Multi-Study Approach to Examine its Relationship with Depression and Opioid Use by Watson et al. - Jobs, Occupations, and Careers on the Line: How Tech-Related Insecurities are related to Workers’ Ethnocentric Attitudes by Bazzoli et al. - When Talent Faces Turmoil: How Occupation Insecurity Fuels Moral Disengagement and Counterproductive Work Behaviors by Probst et al. Attribution matters for the relationship between technological investment and job insecurity Author: Lixin Jiang; The University of Auckland Author: Lucy Xing; The University of Auckland Author: NianNian Dong; University of Science and Technology Beijing Author: Hongmin Yan; University of New South Wales Author: Xiaowen Hu; Queensland University of Technology Unpacking Occupation Insecurity: A Person-Centered Analysis of Educators in the Digital Era Author: Lara Christina Roll; PricewaterhouseCoopers Belgium BV/SRL Author: Ieva Urbanaviciute; Vilnius University Author: Hans DeWitte; KU Leuven Under Economic Pressure: Examining its Relationship with Depression and Opioid Use Author: Gwendolyn Paige Watson; Auburn University Author: Emily G Mattison; Author: Robert R Sinclair; Clemson University Jobs and Occupations on the Line: Tech-Related Insecurities are related to Workers’ Ethnocentrism Author: Andrea Bazzoli; Baruch College of the City University of New York Author: Lara Christina Roll; PricewaterhouseCoopers Belgium BV/SRL Author: Gwendolyn Paige Watson; Auburn University Author: Hans DeWitte; KU Leuven When Talent Faces Turmoil: How Occupation Insecurity Fuels Moral Disengagement and CWBs Author: Tahira M. Probst; Washington State University Author: Rebecca J Lindgren; Washington State University Author: Claudio Barbaranelli; Author: Laura Petitta; Sapienza University of Rome Author: Valerio Ghezzi;

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.362
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it