Praeger International Collection on Addictions
Bibliographic record
Abstract
<JATS1:p>Only the very rare among us are completely unscathed by the effects of addiction - our own, that of a family member, friend, or coworker. Even the addictions of strangers - from the drunk driver or drug addict, to gambling, food, spending, or violence-addicted people - may subject us to dangers, threaten our well-being, and drain money from our pockets. Recent national estimates in just the US show that substance abuse and addiction alone cost taxpayers a total of nearly $500 billion a year. In these volumes, experts from around the world present the newest issues, research, and insights into addictions of all kinds. Led by Angela Browne-Miller, Director of the Metaxis Compulsive and Habitual Behaviors Institute in California, this team of contributors includes scholars and practitioners from the United States, Canada, the United Kingdom, Sweden, Thailand, Africa, and Russia. Topics range from drug addiction among male, female, teen, and aging populations, and among White, Hispanic, Black, Asian, Native American, and other groups, using liquor, cocaine, methamphetamine, khat, and/or other lesser known drugs, to behavioral addictions including online gaming, excessive buying, and eating disorders. Chapters also address issues including addiction as a public health problem and the politics of drug treatment policies.</JATS1:p> <JATS1:p>Treatment methods for addictions, from electrotherapy to holistic approaches are addressed, as are spiritual, psychological, and cross-cultural issues involved. The experts behind these chapters include those from the University of California, Berkeley, Johns Hopkins School of Medicine, and Purdue University, to McGill University, Nottingham Trent University, and the All India Institute of Medical Sciences.</JATS1:p>
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".