A Third Way: Ethics Guidance as Evidence-Informed Provisional Rules
Bibliographic record
Abstract
How should ethics guidance documents be conceived?Benjamin Sachs (2010) suggests that there are two possibilities: They may be attempts to state absolute ethical rules, or they may be recommendations for policies that regulatory bodies should adopt.Since there are cases of clinical research that appear ethical but are inconsistent with the rules, Sachs rejects the first possibility and embraces the second, arguing that ethics guidance should therefore be evidencebased.However, we do not think that this exhausts the ways in which ethical guidance can be helpfully understood, nor do we think that his proposed evidence base for its evaluation is sufficiently broad. 1 Rather than attempt to state exceptionless ethical requirements, ethics guidance documents might be better understood as a set of provisional rules for assessing the ethics of particular research proposals, derived from general ethical principles.Sponsors, investigators, and research ethics committee (REC) members can use sets of such rules to help them think systematically through the ethics of a project.But principles frequently have to be balanced against each other, and provisional rules inevitably admit of particular exceptions.Thus, for example, all else being equal, we think that risks to research participants should be minimized.But sometimes all else is not equal: Perhaps the data obtained in a study would be much more robust if participants' cerebrospinal fluid were analyzed, adding the risks of a lumbar puncture.Whether such exceptions are permitted is a matter of moral judgment.Understood in this way, ethics guidance documents would be making ethical claims, not policy recommendations.But, since they would also admit of exceptions to the rules they state, a case of ethical research inconsistent with a rule would not entail that the rule should be rejected.Instead, a rule should be rejected only if it is shown not to be a good default position or rule of thumb.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.007 | 0.334 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".