Neurobiological research on addiction: What value has it added to the concept?
Bibliographic record
Abstract
Kalant, H. (2015). Neurobiological research on addiction: What value has it added to the concept?. The International Journal Of Alcohol And Drug Research, 4(1), 53-59. doi:http://dx.doi.org/10.7895/ijadr.v4i1.196The initial goal of neurobiological research on addiction was to identify the neural mechanisms involved in the mediation and expression of addictive behavior. More recently, however, it has attributed causal roles to these mechanisms, as illustrated by the definition of addiction as a brain disease caused by chronic exposure to a drug. This concept carries a number of implications that can be assessed experimentally and clinically. None of these implications is borne out by the currently available evidence. The interactions of neuronal systems involved in addiction are also involved in adaptation to experience and environmental change. Much of the neurobiological research to date has not differentiated between causes of addiction, neuronal mechanisms that are activated by them, and risk factors that contribute to individual vulnerability. It has largely ignored the important experiential and environmental influences known to affect the prevalence of addiction in different populations or different times, and it has so far directed much less attention to other forms of addiction-like behavior that do not involve drugs. These failures are not inherent in neurobiological research but require reorientation of objectives, including more emphasis on the study of mechanisms by which environment and experience, including drug experience, can determine whether genetic risk factors are expressed or remain dormant and can direct neuroadaptive mechanisms toward alternative outcomes.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.058 |
| Scholarly communication | 0.010 | 0.040 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".