Bibliographic record
Abstract
Molecular neurobiological studies have yielded enormous amounts of valuable information about neuronal response mechanisms and their adaptive changes. However, in relation to addiction this information is of limited value because almost every cell function appears to be involved. Thus it tells us only that neurons adapt to 'addictive drugs' as they do to all sorts of other functional disturbances. This information may be of limited help in the development of potential auxiliary agents for treatment of addiction. However, a reductionist approach which attempts to analyse addiction at ever finer levels of structure and function, is inherently incapable of explaining what causes these mechanisms to be brought into play in some cases and not in others, or by self-administration of a drug but not by passive exposure. There is abundant evidence that psychological, social, economic and specific situational factors play important roles in initiating addiction, in addition to genetic and other biological factors. Therefore, if we hope to be able to make predictions at any but a statistical level, or to develop effective means of prevention, it is necessary to devise appropriate integrative approaches to the study of addiction, rather than pursue an ever-finer reductive approach which leads steadily farther away from the complex interaction of drug, user, environment and specific situations that characterizes the problem in humans.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.005 | 0.022 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".