THE IMPACT OF COVID ON THE DEVELOPMENT OF HRM IN PUBLIC SERVICE: LESSONS FROM INTERNATIONAL PRACTICE
Bibliographic record
Abstract
COVID has had an immense impact on HRM. The aim of this Paper is to examine international responses and detect best practices. We analyse a variety of methods, techniques, trends and ideas from all over the world. Insights from Hungary, Austria, Germany, Spain, Italy, Portugal, the Netherlands, the United Kingdom, Ireland, the US and Canada are considered. Work has been transported to virtual space. Home office has grown into being the engine of public service development. It is likely that the future will be characterised by hybrid models. Online operation is intertwined with numerous issues, such as: simplification and increased efficiency of procedures, legal regulation of the transformation and data protection. Numerous questions require our answer as regards the use of virtual space: How will teamwork function? What adjustments are required in learning and development schemes? What is the new role of leaders? How can we assure mental health? How do we promote resilience? Another trend concerns digitalisation of recruitment and selection. Digitalisation is spilling over to the neighbouring areas, such as job branding, mobility management and onboarding. How will the post-COVID era look like? The scale of HRM changes ranges from mere adjustment to paradigm shift. Areas of utmost importance include: consequences of accelerated digital transformation, growing importance of IT skills, new methodology for learning and development, demand for resiliency, sustainable development, efficiency, social dialogue as well as restoration of trust between employer and employee. Public service has to adapt to the modified socio-economic environment. Its structure and functioning requires reform. This process incorporates the hope that digitalisation can bring qualitative changes in the functioning of public service. COVID has also brought about a chance to take advantage of the possibilities digital technology can offer. It has enabled us to reinvent the functioning of the state on a higher level.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".