Facing the Fallout: How Technological Advancements and Epidemics Impact Workers’ Economic Stressors
Bibliographic record
Abstract
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;
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".