Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of global research on multigenerational work design using data retrieved from the Scopus database between 2005 and 2025. Using Bibliometrix (R) and VOSviewer, the study maps the conceptual, collaborative, and intellectual structures of the field through keyword co-occurrence, author and institutional collaboration networks, citation analysis, and temporal evolution mapping. The results reveal three major thematic clusters: workforce dynamics (leadership, workplace climate, work environment), generational characteristics (motivation, work values, millennials, personnel management), and intergenerational interaction (social support, human experience, relational factors). The overlay visualization indicates a shift from early descriptive studies of generational traits toward more applied research emphasizing workplace design, digital adaptation, and psychological well-being. Collaboration patterns show fragmented author communities but strong contributions from the United States, Australia, Canada, and India. Highly cited works in the field highlight foundational theories on generational differences, motivational diversity, and intergenerational relations. Overall, this study clarifies the intellectual foundations of multigenerational work research and identifies opportunities for interdisciplinary integration, cross-national collaboration, and applied organizational interventions.
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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 | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.162 | 0.248 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".