What course did the coronavirus pandemic take in Poland and what factors could have influenced it?
Bibliographic record
Abstract
AMA Janik-Koncewicz K, Zatoński WA. What course did the coronavirus pandemic take in Poland and what factors could have influenced it?. Journal of Health Inequalities. 2023;9(2):146-147. doi:10.5114/jhi.2023.133866. APA Janik-Koncewicz, K., & Zatoński, W. A. (2023). What course did the coronavirus pandemic take in Poland and what factors could have influenced it?. Journal of Health Inequalities, 9(2), 146-147. https://doi.org/10.5114/jhi.2023.133866 Chicago Janik-Koncewicz, Kinga, and Witold A Zatoński. 2023. "What course did the coronavirus pandemic take in Poland and what factors could have influenced it?". Journal of Health Inequalities 9 (2): 146-147. doi:10.5114/jhi.2023.133866. Harvard Janik-Koncewicz, K., and Zatoński, W. (2023). What course did the coronavirus pandemic take in Poland and what factors could have influenced it?. Journal of Health Inequalities, 9(2), pp.146-147. https://doi.org/10.5114/jhi.2023.133866 MLA Janik-Koncewicz, Kinga et al. "What course did the coronavirus pandemic take in Poland and what factors could have influenced it?." Journal of Health Inequalities, vol. 9, no. 2, 2023, pp. 146-147. doi:10.5114/jhi.2023.133866. Vancouver Janik-Koncewicz K, Zatoński W. What course did the coronavirus pandemic take in Poland and what factors could have influenced it?. Journal of Health Inequalities. 2023;9(2):146-147. doi:10.5114/jhi.2023.133866.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".