Effetti del Covid-19 sui mercati azionari = Effects of Covid-19 on the stock markets
Bibliographic record
Abstract
L'obiettivo del lavoro di tesi e un analisi di lungo periodo sull'andamento dei mercati azionari; l'intento e quello di effettuare un confronto tra la crisi finanziaria del biennio 2007-2008 con la crisi economica scaturita dall'emergenza Covid-19 nell'anno 2020, in termini di impatto sui mercati azionari. L'interesse nasce dal fatto di confrontare gli effetti di due crisi che sono per natura differenti, una di origine finanziaria (crisi 2007) e l'altra di origine reale (crisi 2020), in modo da capire in che misura possono ritenersi simili. L'analisi verra fatta prendendo come riferimento gli indici generali di borsa di 10 paesi industrializzati (Stati Uniti, Canada, Italia, Germania, Francia, Spagna, Regno Unito, Giappone, Cina, Hong Kong), e ci si concentrera nel quantificare l'effetto contagio delle due crisi, e i principali canali di diffusione.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".