Addiction and emotions: From distress-regulation loops to affective recovery niches
Bibliographic record
Abstract
This paper argues that the emotional dynamics that structure a person’s relationship to drug use and their environment over time are central to understanding how agency is both operative and constrained in addiction. Drawing on qualitative interviews with 69 people with alcohol and opioid addictions, we conceptualize drug use as a form of affective scaffolding – a socially and materially situated strategy for emotion regulation. When this scaffolding becomes inflexible and monopolizing, immediate emotion regulation is prioritized over long-term self-regulation, and an emotional distress and regulation loop results, systematically inclining the agent toward ongoing drug use. We propose that this dynamic gradually constricts a person’s affective repertoire, diminishing the salience of certain diachronic concerns and values, and making it more difficult to sustain behavior oriented toward recovery. The paper closes by introducing the concept of affective recovery niches – structured environments that support alternative patterns of emotion regulation to develop. We focus, in particular, on the important role that other people can play in the recovery niche. Recovery niches disrupt the distress and regulation loop and restore one’s affective repertoire. Thus, they counteract the affective dynamics that we argue keep people stuck in addiction and make new forms of agency possible.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".