Charting the Path for Vulnerable Workers: Theories and Evidence on Overcoming Challenges
Bibliographic record
Abstract
This symposium addresses the underexplored work experiences of vulnerable workers within the management field, a crucial area for advancing corporate social responsibility and highlighting shifting labor market dynamics. By amplifying the voices of vulnerable workers, we strive to ensure their experiences inform workplace policies and organizational practices. The symposium features four scholarly papers that highlight the challenges faced by vulnerable workers, including those with criminal records, disabilities, refugee status, and HIV-positive employees. These contributions advance existing literature by offering a diverse range of theoretical frameworks (i.e., social cognitive theory and self-determination theory, etc.), and promoting an inclusive approach that incorporates the perspectives of underrepresented groups from various regions (i.e., Canada, Philippines, and Lebanon). The goal is to create an academic platform for in-depth analyses, stimulating discussions, and exploratory approaches to addressing the challenges faced by vulnerable workers. By fostering these conversations, the symposium aspires to influence impactful research and practices that contribute to more equitable and compassionate workplaces worldwide. The Impact of Employment Programming on Individuals with a Criminal History Author: Kemi Anazodo; University of Windsor Author: Rosemary Ricciardelli; Author: Christopher Chan; York University Author: Yawo Kobara; University of Windsor Author: Hannah Cook; - Author: Reza Ahmadi; AI for All? Unpacking the Impact of Generative AI for Employees with Disabilities Author: Mary Eve Speach; University of Georgia Author: Katie Badura; Georgia Institute of Technology Author: Brent John Lyons; York University Author: Jean-Marc Moke; Schulich School of Business, York University Work-Related Dehumanization Among Refugees: A Self-Enhancement and Self-Protection Perspective Author: Patricia Tabarani; Author: Teresa Cardador; Author: Constantin Lagios; University of Illinois at Urbana-Champaign Author: Simon Lloyd D. Restubog; University of Illinois at Urbana-Champaign A Self-Determination Perspective on the Job Consequences of Stigmatization Author: Anna Carmella Ocampo; ESADE Business School Author: Fan Xuan Chen; Author: Lu Wang; University of Alberta Author: Simon Lloyd D. Restubog; University of Illinois at Urbana-Champaign Author: Anthony Decoste;
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.027 | 0.068 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".