Bibliographic record
Abstract
This paper analyzes the impact of the COVID-19 government stringency index on foreign investment, at the onset of the pandemic. Through a Robust Least Square regression estimation, we highlight that the relationship between foreign investment and the government containment measures displays a significant cross-country heterogeneity that depends on the level of the pandemic risk in the country. Foreign investors have indeed tilted their asset allocation towards those countries that implemented strong containment measures (as measured by the government stringency index) in the presence of a high risk (as measured by the risk of openness index). Conversely, they have shown a lower propensity to invest in assets issued by countries either adopting weak stringency measures despite a high risk of openness, or implementing drastic stringency measures in the presence of a relatively lower risk of openness. The above findings suggest the following interpretation: the government stringency measures and the pandemic risk have jointly affected foreign investors’ behavior, which appeared relatively more prone towards assets issued by those economies better calibrating the policy interventions according to the pandemic harshness.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".