What Is Pregaming and How Prevalent Is It Among U.S. College Students? An Introduction to the Special Issue on Pregaming
Bibliographic record
Abstract
Pregaming (or prepartying) can be defined as drinking before going to an event or gathering. The heavy consumption of alcohol and resulting negative consequences that are associated with pregaming have prompted scholars to investigate this risky drinking practice. Indeed, research on college pregaming has grown considerably within the past decade, with over 80 articles published since the seminal empirical studies on pregaming were published in 2007. This special issue in Substance Use & Misuse seeks to address a number of topics on pregaming among U.S. college students that are not well understood. The articles in this special issue explore pregaming behaviors among particular subgroups of students (i.e., college freshmen; postgraduates) as well as the following topics as they pertain to pregaming: exposure to trauma, emotion regulation, social norms, pregaming motives, protective behavioral strategies, and intervention efforts. This prologue to the special issue will discuss key points regarding the definition of pregaming, present an overview of the prevalence rates of pregaming among U.S. college students within the past decade, and introduce articles that advance the understanding of factors that contribute to the high-risk drinking context of pregaming.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".