Bibliographic record
Abstract
Addiction is widely taken to involve a profound loss of self-control. Addictive motivation is extremely forceful, and it is remarkably hard to abstain from addictive behaviors. Theories of addiction have sought to explain how self-control is undermined in addiction. However, an important explanatory factor in addictive motivation and behaviors has so far been underexamined: emotion. This paper examines the link between emotion and loss of control in addiction. I use the concept of affective scaffolding to argue that drug use functions as a form of emotion regulation that, especially in certain psycho-socioeconomic conditions, can escalate into what I term addictive affective dependence. Addictive affective dependence is extremely motivating of drug use, and in this way contributes to the agent losing control. An upshot of the paper is that it predicts something that is known to be true about addiction treatment and recovery: strategies that address psycho-socioeconomic conditions are particularly successful in bolstering agency in addiction. Furthermore, my view explains why these strategies work. Thus, the view provides a conceptual framework for existing effective methods of addressing addiction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".