Bibliographic record
Abstract
Purpose This paper aims to advocate the need for educational leadership to understand and consider the immediate role and challenges associated with the unique values and characteristics of an age-diverse population and their impact on teaching and the facilitation of learning. Design/methodology/approach The paper draws on the review of the generational and diversity literatures and related organizational best practices to identify key definitions and empirical findings and to develop recommendations which can be deployed in future research and practice in different types of organizational settings. Findings This paper provides insights into how organizational leaders can promote a multicultural environment that leverages multi-generational differences. Also, the present study offers innovative pedagogical approaches that can help better prepare future business leaders for these challenges. Research limitations/implications The study attempts to reignite the debate through a detailed review that describes the current understanding of generational differences among four generational cohorts. Given the research approach, the recommendations may lack generalizability. Practical implications This paper advocates the need to understand generational differences to manage the challenges associated with differences in attitudes, values and preferences regarding leadership, human resource practices and organizational change initiatives. Social implications Organizations which create environments that are value-based and that support divergent views and values of each of the cohorts, create a positive outcome for both the organization and its employees. Originality/value This paper enhances knowledge and understanding at the theoretical and practical levels, enabling business leaders and faculty to gain insight regarding the generational differences and unique characteristics of four organizational workgroups – Veterans, Baby Boomers, Generation X and Generation Y.
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.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".