DIGITAL TRANSFORMATIONS OF PUBLIC EMPLOYMENT REGULATION: THE IMPACT OF GLOBALIZATION
Bibliographic record
Abstract
The article is devoted to a comprehensive assessment of the manifestations of digitaltransformations in public employment regulation, taking into account the impact ofglobalization. The main components of the impact of globalization processes on the digitaltransformation of public employment policy have been identified. These include: thereassessment of the functional role of the state as a labor market regulator; the growing needfor the unification of digital standards; the stimulation of the development of modern digitalemployment services; and the necessity of harmonizing national digital solutions with globalcybersecurity frameworks. A conclusion has been drawn regarding the transformation of the concept of «publicemployment regulation» – from reactive responses to changes toward the strategic digitaldesign of national policies aimed at adapting to a dynamic, globally integrated digital economy. The article analyzes the key components of employment regulation and labor marketsystems in the EU, the USA, Canada, and Ukraine. It emphasizes that the digital transformationof the economy serves as a catalyst for the modernization of state labor policy. The study explores the primary changes caused by the implementation of digitaltechnologies in Ukraine’s employment regulation sector – namely, the introduction ofelectronic services by the State Employment Service of Ukraine (SESU), the increasingintegration of SESU’s information systems with other national registries, and the accelerationof internal process automation and optimization for addressing employment issues. The main effects of implementing these digital services are outlined, including thereduction of workload on employment centers, the elimination of excessive paperdocumentation, and the improved accessibility of employment-related information. Fourdistinct stages of the digitalization process of public employment regulation in Ukraine areidentified and characterized. The challenges of implementing digital transformations in Ukraine have beenemphasized, including: the digital divide; cybersecurity and personal data protection; theadaptation of legislation and regulatory frameworks; insufficient funding and human resourcecapacity; poor quality and limited functionality of electronic services; and the socio-economicconsequences of the war. Priority directions for updating public employment regulation in Ukraine, in the contextof adapting to the digital trajectory of national economic development under conditions ofinstability, have been identified. These include: strengthening cybersecurity and dataprotection, enhancing the digital literacy of the population, updating the legal and regulatoryframework, developing systems of professional training and retraining, and accelerating thedevelopment of inclusive digital services. Keywords:digitalization, employment, globalization, public employment regulation,transformation, labor resource flows, labor market, international labor market.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".