The Voluntary Nature of Decision‐Making in Addiction: Static Metaphysical Views <i>Versus</i> Epistemologically Dynamic Views
Bibliographic record
Abstract
The degree of autonomy present in the choices made by individuals with an addiction, notably in the context of research, is unclear and debated. Some have argued that addiction, as it is commonly understood, prevents people from having sufficient decision-making capacity or self-control to engage in choices involving substances to which they have an addiction. Others have criticized this position for being too radical and have counter-argued in favour of the full autonomy of people with an addiction. Aligning ourselves with middle-ground positions between these two extremes, we flesh out an account of voluntary action that makes room for finer-grained analyses than the proposed all-or-nothing stances, which rely on a rather static metaphysical understanding of the nature of the voluntariness of action. In contrast, a dynamic concept of voluntary action better accounts for varying levels of voluntariness of the person with an addiction which takes into consideration internal (e.g. cravings) and external (e.g. perceptions of degrees of freedom related to different options) determinants of choice. Accordingly, like other components of autonomous choices such as level of information, voluntariness can fluctuate. Therefore, there are important implications for research and clinical ethics in matters of consent, recruitment, and therapeutic approaches. Overall, our proposal is inspired by a pragmatist understanding of voluntary action, notably with respect to how voluntariness is both informed by actions and experiences that shape one's view of the world.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.079 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".