Review of top five financial markets during the pandemic times
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
The purpose of this paper is to jot down the devastating impacts of COVID-19 towards the top five financial markets of the world and to see how they reacted back in different phases of COVID-19 from start till July 2020. The review is based on the financial market news, blogs, the governmental, and other financial bodies’ websites. The effects of the pandemic are like the damage never seen before in a much shorter time, vanishing a quarter portion of wealth in about a month and creating continuous uncertainties for investors throughout. China despite being the virus origin still performed well and better among all top markets whereas the rest all the stock exchanges remained inconsistent. This paper is the first of its kind to review the COVID-19 effects on the top five global stock markets and the governmental responses towards them. The study along with contributing to the existing literature is also assisting investors, analysts, specialists, and authorities to analyze their opinions with respect to stock markets performances, government responses, and their future market-related decisions.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it