Balancing Risk and Reward in the Time of COVID-19: Bridging the Gap Between Public Interest and the 'Best Interests of the Corporation'
Bibliographic record
Abstract
The scale of the global COVID-19 pandemic has made plain that business organizations have a key role to play in supporting public health efforts to contain the virus and follow social distancing. Directors and officers have been called upon to make proactive decisions about risk reduction that may hurt the bottom line (or simply diverge from established practice) but are the right thing to do. However, corporate law is permissive and tends to avoid dictating what should be done, so long as it is in the “best interests of the corporation”. Uncontrolled outbreaks of the virus in certain sectors of the economy deemed essential raise the difficult question of whether this flexible standard promotes an appropriate balance between economic viability and the legal pursuit of profit on the one hand and fundamental values such as the protection of human life and security on the other. In this paper, I reflect on how the pandemic situation brings this tension into sharper relief and exposes an accountability gap. I suggest that bridging this gap may be possible if we are prepared to recognize more explicitly that sometimes what is best for the corporation to protect the public interest.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".