Business ethics in the era of COVID 19: How to protect jobs and employment rights through innovation
Bibliographic record
Abstract
The pandemic situation generated by the novel coronavirus virus (COVID-19) created several moral and economic dilemmas. While trying to save many local and world economies, entrepreneurs, leaders, and policymakers faced the challenges of managing the resultant economic and financial disruptions and risks coupled with the moral obligation to observe business ethics. This research is based on a documental collection, revision, and analysis of relevant and emerging literature to catch the best practices and experiences adopted by various governments and businesses, especially in western countries, to protect the jobs and employment rights of workers. Among other things, this study urges social policymakers to adopt innovative mechanisms and programs to not only protect the rights of employees but also help maintain jobs during pandemic situations and economic crises The research suggests that adhering to business ethics will enhance the use of technology and boost the sense of innovation and creativity of both employees and their organizations. The importance of the collaboration between public Administrators, policymakers, entrepreneurs, and employees to maintain the fundamentals of business ethics and protect employees’ rights is adjudged to be critical to a speedy recovery from the losses and disruptions caused by the pandemic.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".