Glaser, Frederick (Fred) interview conducted by Campbell/Spillane
Bibliographic record
Abstract
Frederick B. Glaser received his M.D. from Harvard University in 1959 and then served his residency at the U.S. Public Health Service Narcotics Hospital in Lexington, Kentucky. In his interview, he recalls many aspects of his time at Lexington, including the bias against the substance use field at that time, memorable patients, and relations among staff from different disciplines. Glaser went on to have a distinguished career in the field of substance abuse, including the study of opioids and alcohol. In particular, he argued for a range of treatment approaches to address the complex problem of excessive drinking. He has held a variety of positions in the United States and Canada, serving as the director of the University of Michigan Substance Abuse Research Center from 1989 to 1994 before becoming Professor of Psychiatry and Director of the Division on Substance Abuse at the East Carolina University School of Medicine. He is now retired in Greenville, North Carolina. Source: Transcript.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".