Bibliographic record
Abstract
Bioethical discussions surrounding the use of placebos in clinical practice have long revolved around the moral permissibility of deceiving a patient if it is likely to benefit them. While these discussions have been insightful and productive, they reinforce the notion that placebo effects can only be induced through deception. This paper challenges this notion, looking beyond the paradigmatic clinical encounter involving deceptive placebos and towards many other routes that bring about placebo effects. After briefly describing the bioethical terrain surrounding the deceptive use of placebos in clinical practice, section 1 offers an examination of the various mechanisms known to contribute to placebo effects: classical conditioning, expectations, affective pathways, open-label placebo treatments, and additional factors that do not fall easily into a single category. The following section explores how each of these routes can be harnessed to bring about clinical benefits without the use of deception. This provides grounding for reconceiving of the placebo effect as a clinical tool that is not always in conflict with patient autonomy and can even be seen as a source of agency. In the final section, implications of the shift away from seeing placebos as necessarily deceptive are discussed. These include the necessity of looking beyond the clinical encounter and mainstream medicine as the primary sites of placebo responses, how important acknowledging the limits of placebo effects will be when we do so, as well as the difficulties of disentangling agency, responsibility, and blame within medicine.
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 | 0.085 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".