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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".