El necesario cambio de paradigma en el estudio de la adicción: inconsistencias y falacias del modelo de enfermedad cerebral de la adicción:
Bibliographic record
Abstract
The Brain Disease Model of Addiction (BDMA) has been the dominant paradigm since its official proclamation by the National Institute of Drug Addiction (NIDA) a quarter of a century ago. However, all its principles have been repeatedly falsified and none of the benefits proposed by its authors have been achieved. Its survival is based on the unconditional support of the pharmaceutical industry and on the management of funds that NIDA allocates as a priority to studies that verify its hypotheses, as well as disregard for all research that questions its principles. Following Popper, the correct procedure is not verificationism, but the falsification principle, which forces the discarding of refuted hypotheses. And, following Kuhn, when a scientific paradigm does not meet the requirements, it must be replaced by another that surpasses the discarded one. This article reviews the inconsistencies of the BDMA and the fallacies on which its hegemony, now firmly questioned, has been based.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.107 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".