The effect of policy response to the COVID-19 pandemic on GDP growth, an analysis with variations over time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The study is aimed at exploring the effect of three policies, social distancing, economic support to households and business, and vaccination on GDP, adopted by governments to mitigate the effects of the of COVID-19 pandemic. The analysis applies a data set scoping from the first quarter of 2020 to the third quarter of 2021 across OECD countries. The methodology incorporates interactive time dummy variables to capture variations of the effects over time; in addition, control factors comprising social, medical, demographic, besides health service sufficiency are incorporated in the analysis to disentangle the effect of policy response variables. The result indicates that the impact of policies can vary over time and hence, it is important the governments conduct strategies to keep effectiveness of the policies.</p>
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it