The Social Origins of Alcoholism: Abraham Myerson and the Significance of Drinking Norms in Alcohol Addiction, 1938–1946
Bibliographic record
Abstract
In the 1940s, Abraham Myerson's work on drinking norms in the USA was central to reorienting the approach medical and scientific experts adopted when studying and treating alcoholism. A leading psychiatrist and neurologist from Boston, Myerson argued that tensions between alcohol's ability to satisfy a pleasure-seeking drive and the rise of asceticism had generated ambivalent social attitudes, traditions and expectations towards drinking. This article explores how Myerson identified and employed social factors to uncover the relationship between ambivalent drinking norms, one's gender, ethnic or religious background, and whether one would drink to excess. In doing so, it will illuminate how Myerson's innovative efforts to highlight the role of social attitudes and traditions in alcoholism ultimately helped shape the approach of medical science to the alcohol problem.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".