Riflessioni a confronto sul fenomeno COVID-19 e l’influenza spagnola
Bibliographic record
Abstract
Le epidemie, oltre a causare morti e paura, hanno causato nella storia trasformazioni sociali, politiche, economiche e demografiche. Sono state trasportate dalle carovane, dai battelli o dagli eserciti in guerra. Sul finire del 2019 il Covid-19 è stato identificato dalle autorità sanitarie della città di Wuhan, capitale dello Hubei in Cina. La sua diffusione nei primi mesi del 2020 ha indotto l'Organizzazione Mondiale della Sanità a chiedere misure preventive, di sorveglianza attiva, l'isolamento dei casi e l'11 marzo 2020 è stata dichiarata la pandemia. In queste brevi riflessioni, sviluppate durante le prime settimane di diffusione del virus in Italia, si è tentato di ricostruire un quadro generale della pandemia in rapporto con l'influenza spagnola della quale è stata fatta una breve ricostruzione storica.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".