Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Janik-Koncewicz K, Basiak-Rasała A, Zatońska K, Karczewski M, Zatoński WA. Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic. Journal of Health Inequalities. 2022. doi:10.5114/jhi.2022.121031. APA Janik-Koncewicz, K., Basiak-Rasała, A., Zatońska, K., Karczewski, M., & Zatoński, W. A. (2022). Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic. Journal of Health Inequalities. https://doi.org/10.5114/jhi.2022.121031 Chicago Janik-Koncewicz, Kinga, Alicja Basiak-Rasała, Katarzyna Zatońska, Maciej Karczewski, and Witold A Zatoński. 2022. "Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic". Journal of Health Inequalities. doi:10.5114/jhi.2022.121031. Harvard Janik-Koncewicz, K., Basiak-Rasała, A., Zatońska, K., Karczewski, M., and Zatoński, W. (2022). Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic. Journal of Health Inequalities. https://doi.org/10.5114/jhi.2022.121031 MLA Janik-Koncewicz, Kinga et al. "Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic." Journal of Health Inequalities, 2022. doi:10.5114/jhi.2022.121031. Vancouver Janik-Koncewicz K, Basiak-Rasała A, Zatońska K, Karczewski M, Zatoński W. Considerations on mortality in 2020 in Poland in the context of the coronavirus pandemic. Journal of Health Inequalities. 2022. doi:10.5114/jhi.2022.121031.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".