Moving beyond the “eating addiction” versus “food addiction” debate: Comment on Schulte et al. (2017)
Bibliographic record
Abstract
In a recent commentary, Schulte et al. (2017) argued that addictive-like eating should be conceptualized as a substance use disorder rather than a behavioural addiction, and noted that many parallels that Hebebrand et al. (2014) drew between addictive-like eating and behavioural addictions apply likewise to substance use disorders. However, we argue that many of the arguments advanced by Schulte et al. (2017) in support of a substance-based food addiction model, including the important role played by ingested substances, are nonspecific. That is, these arguments apply equally well to behavioural addictions and other mental disorders, notably eating disorders, which raises the question of whether the phenomenon of addictive-like eating is encompassed by existing eating disorder diagnoses. Similarities between addictive-like eating and substance use, no matter how compelling, do not ensure the validity or clinical utility of a substance-based food addiction model and should not drive the conceptualization of addictive-like eating. The present commentary discusses problems with Schulte et al.'s (2017) arguments for substance-based food addiction, and draws attention to alternative conceptualizations of addictive-like eating which risk being overlooked when this conversation is framed as a dichotomous debate between the food and eating addiction models.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.106 | 0.094 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".