Exploring responsible leadership in smart city human resource management
Bibliographic record
Abstract
Smart cities represent a new urban development model, which uses information and communication technologies to enhance the quality and performance of urban services to use limited resources better, thereby reducing emissions and costs. For smart cities to be successfully developed, HR must also be managed smartly, resulting in Smart City Human Resource Management. Smart City Human Resource Management is performed in the context of a smart city by engaging in the managerial tasks of planning, organising, directing, and controlling city operations related to the procurement, development, maintenance, and utilisation of a smart city workforce. In other words, the human resource management of smart city workers, who are engaged in the work processes of smart city jobs, is Smart City Human Resource Management. Smart City Human Resource Management is likely to be increasingly important in the successful development and operation of smart cities. This study examines human resource management through the lens of responsible leadership. Leadership is about articulating a vision and setting direction, aligning people, motivating and inspiring people, coping with change and providing useful influence. Responsible leadership adds to this understanding by asserting that leadership is not just about realising a vision or strategic goal but is about discovering a vision or goal that has positive intended consequences for the broader stakeholders of a firm. We argue that responsible leadership should be explored more in the context of human resource management. We explore responsible leadership in the recruitment and selection process and the training and development process of human resource management in Smart City Human Resource Management.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".