Sustainable Human Resource Management, Unions and Equality-Enabling Practices: A Framework-Based Systematic Literature Review and Directions for Future Research
Bibliographic record
Abstract
In response to Sustainable Development Goals 5 and 10 (SDGs), organizations are collaborating with unions to integrate sustainability into their strategies by adopting socially responsible HR practices for social equity and equality. In this study, grounded in social justice ideology, we explore the antecedents, decisions and outcomes of sustainable HRM practices that promote workplace equity and equality with union support. We used a framework-based systematic literature review (SLR) method, guided by the antecedents-decisions-outcomes (ADO) framework of Paul et al . (2023), and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols to ensure comprehensive and transparent reporting. Using the ADO framework, we identified organizational culture and values, stakeholder engagement, union trends, legal framework and regulations, collective agreements and company size as “antecedents.” “Decisions” encompassed inclusive recruitment and hiring practices, equal pay and compensation initiatives, diversity and inclusion training, flexible work arrangements and mechanisms for conflict resolution and grievance handling. “Outcomes” included improved employee well-being, enhanced productivity and performance, greater support for diversity and inclusion and a positive impact on employee health and safety. Each of the explored practices corresponded to one of three types of social justice. We suggest several avenues for future research by identifying critical gaps in theory and practice.
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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 | 0.089 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.036 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".