El impacto económico de la pandemia de COVID-19
Bibliographic record
Abstract
[ESP] En este trabajo realizo la recopilación de variables económicas tras un año conviviendo \ncon el Covid-19, desde que se diagnosticó el primer caso positivo, pero los efectos \neconómicos siguen basándose en predicciones. La incidencia de contagios a lo largo de \ntodo el globo ha causado una crisis mundial donde todas las economías han sido afectadas \nen mayor o menor medida, siendo la expansión y perduración del coronavirus lo que \ndeterminará cuando será posible la vuelta a la normalidad. \nLos indicadores pronostican a corto plazo un escenario de crecimiento, pese a ser uno de \nlos países más afectados, gracias a las políticas económicas y sociales tomadas en el seno \nde la Unión Europea y en el Gobierno de España. \nSerá a largo plazo cuando se podrá analizar con exactitud el alcance de la pandemia ya \nque son muchos los que prevén la vuelta de “los felices años veinte”, años de prosperidad \ny crecimiento, o, por el contrario, una caída del consumo, inversión y un déficit en la \nbalanza comercial. [Eng] n this work I carry out a collection of economic variables after a year living with Covid- \n19, since the first positive case was diagnosed, but long-term economic effects remain in \nuncertain. The incidence of infections throughout the globe has caused a global crisis in \nwhich all economies have been affected to a greater or lesser extent, and the expansion \nand persistence of the coronavirus will determine when it will be possible to return to \nnormality. \nShort term analysis forecast a growth scenario for Spain despite being one of the most \naffected countries, thanks to the economic and social policies taken within the European \nUnion and the Government of Spain. \nIt will be in the long term when the scope of the pandemic can be accurately analysed \nsince there are many who foresee the return of the “happy twenties”, years of prosperity \nand growth, or, on the contrary, a drop in consumption, investment, and a deficit in the \ntrade balance
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".