Author’s Affiliation: Alberta Alcohol and Drug Abuse Commission
Bibliographic record
Abstract
Several years ago, in the course of looking at some addictions resources, I ran across the concept of Attribution Theory, and really freaked myself out. It led me to a whole body of research literature I hadn’t had any idea existed, a kind of subversive thread in the literature. Essentially, it called into question whether addiction even exists. Now, my immediate reaction was worry. Addiction needs to exist. I mean, I work in the addictions field, and my bosses are not stupid; if I’m treating something that doesn’t exist, sooner or later they are going to notice. And I have mortgage payments to make, in Calgary, which would become a problem quick. So it’s important that whatever I find out means that I am working in a field that actually exists, or it’s back to washing dishes – a skill set my wife assures me has completely atrophied. I should take a minute and say that when I talk about Addiction, or Dependence, or Abuse, I’m talking about any of the following ideas: • That problem behaviours which coincide with substance use are at least partly to do with properties of the substance, • That some substances are more habit-forming than others,
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.533 | 0.168 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".