Reframing the science and policy of nicotine, illegal drugs and alcohol – conclusions of the ALICE RAP Project
Bibliographic record
Abstract
In 2013, illegal drug use was responsible for 1.8% of years of life lost in the European Union, alcohol was responsible for 8.2% and tobacco for 18.2%, imposing economic burdens in excess of 2.5% of GDP. No single European country has optimal governance structures for reducing the harm done by nicotine, illegal drugs and alcohol, and existing ones are poorly designed, fragmented, and sometimes cause harm. Reporting the main science and policy conclusions of a transdisciplinary five-year analysis of the place of addictions in Europe, researchers from 67 scientific institutions addressed these problems by reframing an understanding of addictions. A new paradigm needs to account for evolutionary evidence which suggests that humans are biologically predisposed to seek out drugs, and that, today, individuals face availability of high drug doses, consequently increasing the risk of harm. New definitions need to acknowledge that the defining element of addictive drugs is 'heavy use over time', a concept that could replace the diagnostic artefact captured by the clinical term 'substance use disorder', thus opening the door for new substances to be considered such as sugar. Tools of quantitative risk assessment that recognize drugs as toxins could be further deployed to assess regulatory approaches to reducing harm. Re-designed governance of drugs requires embedding policy within a comprehensive societal well-being frame that encompasses a range of domains of well-being, including quality of life, material living conditions and sustainability over time; such a frame adds arguments to the inappropriateness of policies that criminalize individuals for using drugs and that continue to categorize certain drugs as illegal. A health footprint, modelled on the carbon footprint, and using quantitative measures such as years of life lost due to death or disability, could serve as the accountability tool that apportions responsibility for who and what causes drug-related harm.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".