Bibliographic record
Abstract
This study was aimed to survey the status of drinking behaviors of college students in Korea. Also one of the main purpose was to analyze the reasons why college students drink too much and how many students are binge drinkers.The survey procedures for the college students are as follows;Instrument: 80% of Harvard College Alcohol Study Questionnaires were used in KCAS Questionnaires.The samples: A national samples were selected from Korean Council on Educations list of accredited 4-year colleges by using probability proportionate to size of enrollment, types, and districts.Participating Universities: Considered districts and types of University, we selected 63 Universities randomly.We sent letters asking to be participated with this study to the presidents of each University. Participating letters from 55 Universities were arrived first. We called to the 8 University Presidents directly to join this study. 5 among the remaining 8 Universities agreed to participate with this survey. We decided to survey 60 University.Sampling and procedures: We selected samples in the participating Universities. First, we selected colleges, departments, and grades in participating University. Second, we sent proportionally assigned Questionnaires to the registrars or faculties. They returned the Questionnaires to my University.Sampling period: From May 15, 2003 to June 15 The main results are as follows;First, frequent binge drinking rate of College students is very high. About 65.7% of students are binge drinkers, and 33.2% are frequent binge drinkers.Second, frequent binge drinking rate of 24 or more aged students is higher than under ages. This is different with US and Canada's situations.Third, being different with US situation's, club affiliation doesn't affect to binge drinking greatly.Forth, also being different with US and Canadas situation's, the students who reside in the University are less likely to take binge drinking than the students who reside off-campus.Sixth, most of alcohol-related problems of Korean students are higher than those of US students, except sexual activities, drove after drinking, and argue with friends.Seventh, although Korean students drink more than US students, Korean students are 0less likely to have secondhand binge-drinking effects. Most of Korean people are more generous about the others drinking and drinking problems. And most of Korean people are thinking importantly of drinking socializing. So they dont think those things as problems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".