Health Security as a Global Public Good in the Conditions of the Revolution 4.0
Bibliographic record
Abstract
Objectives: Although the concept of health security is becoming accepted in public-health-related literature and practice, there is no full agreement on the scope and content. The aim of this paper is to draw attention to the definition of health security and its role within the Revolution 4.0. Research Design & Methods: This is a theoretical article and as such addresses a problematic situation concerning missing standards in health security and the Revolution 4.0. Findings: The WHO (2018) has stated unequivocally that ‘functioning health systems are the bedrock of health security’. The authors attempt to prove that health security in the conditions of the Revolution 4.0 needs to be defined more precisely and has to be implemented as a global public good nationwide with accepted minimal standards. Implications / Recommendations: H ealth security belongs to the sphere equally important to that of the Revolution 4.0. A concept of Health Security that is not widely accepted and implemented creates a problematic mélange for employees as well as for industrial development. These features will be considered. Contribution / Value Added: This paper tries to underline the relative shortage of common agreements on health within the Revolution 4.0.
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".