Bibliographic record
Abstract
In cases involving the rescue of people in need of immediate medical care, it is often thought that the responsibility to save the lives of the imperilled falls to advanced professionals, such as paramedics, doctors, nurses, etc. This tells only part of the story, however, as in many cases the first point of contact for a person under duress is non-professional bystanders – average people with often little to no training in first aid or medicine. If the first point of contact is the bystander, do these bystanders have an obligation to help? Even if we assume that it is good to help people in need, the answer is not immediately obvious. Matters become more complicated when the bystander does have training that would make their intervention efficacious in helping the victim. Are they expected to help because they are trained and could presumably help more? \nThis thesis seeks to examine this question and argue the following two conclusions: first, in terms of rescue cases, trained bystanders, whom I call informed rescuers, are morally required to act because of their training; and second, given the special role of knowledge in rescue, those who do not possess training in first aid can be held morally blameworthy for failing to know how to act in rescue cases. Because of this, everyone ought to learn basic first aid.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".