Bibliographic record
Abstract
Even in a pandemic there seem to be inherent conflicts of interest between the individual and societal consequences of remedial actions and strategies. Actions taken in the sole interests of patients, as required by the Hippocratic oath, can have broadly inconvenient economic implications for the State. ("Average" benefits for a population can impose individual inconveniences for the vulnerable.). Understandably these decisions are not normally made explicitly and transparently by governments. This leads to seemingly illogical and inhumane strategies which are not understood and hence mistrusted and often ignored by the public. Vaccination sentiments on social media are often an unwanted symptom of this dilemma. This article outlines and discusses a number of examples of such situations with a focus on ethical aspects. It concludes that each case must be considered individually as to the issues that need to be weighed in these difficult decisions; and that there are no clear and universally acceptable ethical solutions. What can be learned from the COVID-19 crisis is that short term utilitarianism has consequences that in the eyes of the population are unacceptable. This lesson seems equally valid for cost benefit evaluations regarding other risks, such as from hazardous industries, flood defenses, and air transport. Decisionmakers and politicians can learn that persuasion only goes so far. In the end the people appear to prioritize in terms of deontology.
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.
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 | 0.079 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.015 | 0.016 |
| 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".