A better, human-centered path forward
Bibliographic record
Abstract
Abstract The main purpose of the paper is to highlight the increased recognition and growing importance of human-oriented leadership in a constantly changing world. The COVID-19 pandemic has revealed many management shortcomings in developing current and future leaders. The difficulties creating new business models, adapting to new working environments, and fostering humane corporate cultures underscore the short- and long-term challenges. The literature review offers multiple perspectives on our humanity and its inherent challenges, measures of effective leadership, approaches to leadership development, and what seems to be emerging as a values-based, human-centered approach to what effective leadership looks like. The paper suggests a holistic interdisciplinary approach aiming to support partnership between the international business community and academic environment. The methods include: a literature review as background and context; a case study; a comparative analysis of best practices in four countries; and a statistical analysis of economic practices in Romania. The main results indicate: an increased acknowledgment of human-centered leadership practices as essential to effective leadership; the use of a 70-20-10 model of leadership development as a best practices approach; and the adverse effects of the pandemic on the Romanian economy. Our conclusions reaffirm: the power of the human-centered approach to how leaders have to perform; the need to rethink how leadership development should be done, and the ongoing challenge of choosing to invest in leadership as a sound business decision. Authors conclude that the changes in mindset, priorities, decisions, and practices will be challenging for leaders to make. Suggestions are offered about this new path.
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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".