Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research
Bibliographic record
Abstract
This commentary supports the argument that there is an increasing tendency to subsume a range of excessive daily behaviors under the rubric of non-substance related behavioral addictions. The concept of behavioral addictions gained momentum in the 1990s with the recent reclassification of pathological gambling as a non-substance behavioral addiction in DSM-5 accelerating this process. The propensity to label a host of normal behaviors carried out to excess as pathological based simply on phenomenological similarities to addictive disorders will ultimately undermine the credibility of behavioral addiction as a valid construct. From a scientific perspective, anecdotal observation followed by the subsequent modification of the wording of existing substance dependence diagnostic criteria, and then searching for biopsychosocial correlates to justify classifying an excessive behavior resulting in harm as an addiction falls far short of accepted taxonomic standards. The differentiation of normal from non-substance addictive behaviors ought to be grounded in sound conceptual, theoretical and empirical methodologies. There are other more parsimonious explanations accounting for such behaviors. Consideration needs to be given to excluding the possibility that excessive behaviors are due to situational environmental/social factors, or symptomatic of an existing affective disorder such as depression or personality traits characteristic of cluster B personalities (namely, impulsivity) rather than the advocating for the establishment of new disorders.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.049 | 0.061 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".