Bibliographic record
Abstract
Placebos have the potential to improve patient outcomes and promote our well-being. They are important to the methodology of clinical trials, and they disturb the boundaries of what counts as ‘real’ medicine. In this dissertation I present the Social Positioning Account of placebos wherein placebos are partially constituted by the social context in which they are administered, and furthermore have a distinctive normative profile. An intervention only counts as a placebo when the client perceives themselves to be in a treatment context, and placebos are held to a norm such that they are better the more they resemble an extant standard treatment. I argue that the current state of empirical literature does not support the assumption that there is a unique causal driver for placebo effects. Thus, the Social Positioning Account is causally neutral—it does not define placebos in terms of a particular class of causes or causal mechanisms.The ethics of placebo administration intersects with broader questions within bioethics. In research settings, placebos are particularly important in the running of Randomized Controlled Trials (RCTs). I therefore widen the scope of discussion and develop a framework for analyzing the ethical good standing of RCTs. I use that framework to examine a cluster of issues that arise in the running of RCTs, of which placebo administration is one. The placebo effect is also relevant to clinical practice; practitioners must balance the ethical obligation to disclose information to a patient with the assumed health risks or benefits consequent of that disclosure. Although withholding information can sometimes violate the duty to promote patient autonomy, I argue that there are surprising cases in which autonomy is promoted by withholding information and apply this insight to cases of placebo administration in clinical practice.
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 | 0.104 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.080 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.020 |
| 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".