COVID-19-Shock: Considerations on Socio-Technological, Legal, Corporate, Economic and Governance Changes and Trends
Bibliographic record
Abstract
Concurrent with an already ongoing digitalization trend, the COVID-19 pandemic implies widespread changes for individual decision makers in their adoption of technological assistance but also in giving up decision making to Artificial Intelligence (AI). Economic facets of collective learning processes during the coronavirus crisis are outlined with a special emphasis on the currently ongoing digital disruption. As a widespread external shock to the world economy and legal order, COVID-19 affects corporate conduct profoundly. The legal implications and societal changes’ impetus on corporate conduct are depicted in order to derive future corporate governance prospects. From an evolutionary dynamics market perspective, a trends prediction sheds light on what kind of firms are likely to fail and which may survive and which ones could thrive in the following years and decades to come. International differences in the handling of COVID-19 are highlighted in order to envision future global public healthcare. The recommendations address the importance of well-calibrated goals to cure our contemporary humankind and protect our future common world population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".