Bibliographic record
Abstract
The particular allure of amphetamine sulphate is that it is an antidote to so many unwelcome human conditions. As a stimulant that affects the nervous system rather like adrenaline, it boosts energy levels and alertness, elevates mood, combats obesity and even helps nasal congestion. The medical profession in the UK recognized its value and prescribed it extensively from the 1930s. Those fighting in World War II were often kept going with a supply of ‘pep-pills’, as they became known. Long-distance lorry drivers, women with weight problems, athletes, students taking examinations-all have used it, and for some time it was the only medication for depression. Despite some unpleasant side effects such as amphetamine psychosis, per iodic aggressive outbursts and cardio-vascular disorders that were observed when taken to excess, the demand for amphetamines has remained high. However, the potential for abuse was ultimately acknowledged by the authorities and in 1957 the drug became available only on prescription. There followed an ‘epidemic’ of illicit use in the 1960s, particularly among some young adults. The situation was much the same in Sweden, Japan, Canada and the United States. Today, amphetamine sulphate’s use is ubiquitous across much of the developed world. Much cheaper than cocaine, which tends to be regarded as the ‘champagne’ of stimulants, it is second only to cannabis in the extent of its use.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".