Bibliographic record
Abstract
Battling a Thousand Points of Might Jonathan Kimmelman (bio) When placed between disclosure and capacity—its loud and unruly siblings—voluntariness can be seen as the neglected middle-child of informed consent. By my count of articles invoking consent on PubMed, for every one that mentions voluntariness, roughly three mention capacity. This seems odd; after all, "voluntary" is the second word (after "the") in the oldest formal statement on research ethics, the Nuremberg Code. Into this breach step Paul Appelbaum, Charles Lidz, and Robert Klitzman to lay out a conceptual model of voluntariness, with the ambitious goal of developing instruments for assessing its constraint. They begin with a brief consideration of neuroscientific conceptions of voluntariness, which often question its very possibility by pointing out that reasoned judgments are preceded by unconscious cerebral activity. Volition, according to this view, is at best a fleeting moment where individuals override prefigured decisions; at worst, it is a trick of the brain designed to flatter the self. On their face, however, neuroscientific accounts seem unsuited to adjudicating consent validity because they rule out moral distinctions between decisions coerced by threat of harm and those made with what Hans Jonas called "maximum spontaneity." Sensibly, then, Appelbaum and colleagues opt for an alternative. They settle on a legally grounded conception of voluntariness, holding that decisions become involuntary when materially affected by "external, intentional, [and] illegitimate" influences. Thus, for example, a reluctant subject who enrolls in a study because his physician threatens to end his care has clearly made an involuntary decision. The same subject who enrolls after his physician explains the social value of the research makes a voluntary decision, however, because appeals to common values are generally legitimate. This formulation has several compelling features. First, it is tried and true (though perhaps not tried in the literal sense: the authors note a "remarkable paucity of litigation" testing it). Second, it helps demarcate many types of involuntariness that historically have raised concerns. Third, their approach would seem to enable the kind of packaging and portability needed for an instrument of consent "epidemiology." Nevertheless, the legal model leads to some counterintuitive propositions. Imagine a cancer patient who would strongly prefer palliative care only but is cajoled by a spouse to enter a burdensome drug trial. Under the legal formulation, spousal pressure is legitimate, so the decision to enter is not involuntary. Yet doesn't it seem odd to consider this manipulated decision voluntary simply because the manipulation is not legally actionable? Shouldn't a conscientious clinician worry about the voluntariness of her patient's choice? Another concern with the formulation is that, though it tells us what voluntariness is not, it stops well short of either describing what voluntariness is or articulating an ideal toward which clinicians should strive. Related is the question of whether frameworks devised for legal disputes are fitted to the typical consent encounter. For good reason, law tends to establish high thresholds for determining misconduct: intervention by the state into private matters is cumbersome and expensive, and courts need tests that allow transparent, expedient, and decisive resolution. Consent decisions in research, however, would seem to require more refined tools. Last is the project of indexing. If we measure voluntariness prospectively, how can we attribute material cause when events have not yet occurred? And if we measure it retrospectively, needn't we worry that patients will confirm voluntariness when outcomes are favorable and assert manipulation when they are not? And how does this approach respond to material but unconscious influences of external, intentional, possibly illegitimate factors like framing and what Richard Thaler and Cass Sunstein call "choice architecture"? Still, none of this seriously undermines this effort at domestication. The program arrives during a seeming epidemic of what Appelbaum et al. call "situational constraints" that "set the stage for . . . intentional manipulation." This includes the placement of drug studies in relatively impoverished settings and occasional proposals within bioethics to tweak volunteers' perceptions by manipulating affect. More broadly, commentators advise scientists to "frame their information," political candidates to "appeal to the gut," and policy-makers to configure decision-making environments—all to influence decision-making without reasoned argument. An epidemiology of voluntary decision-making might help us...
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".