Bibliographic record
Abstract
Callinan, S. (2014). Alcohol’s harm to others: Quantifying a little or a lot of harm. The International Journal Of Alcohol And Drug Research, 3(2), 127-133. doi:http://dx.doi.org/10.7895/ijadr.v3i2.160Aim: Harm to others from alcohol consumption has become a World Health Organization research priority and the subject of current or planned research in over 20 countries. The aim of the current study is to compare the efficacy of two measures commonly used to ascertain the subjective level of harm experienced by respondents that is attributable to the drinking of others.Design: A cross-sectional survey.Setting: Australian respondents were recruited using computer-assisted telephone interviewing.Participants: 448 adult respondents were asked about their experience of harm attributable to the alcohol consumption of others.Measures: Respondents were asked whether they were harmed a little or a lot by the drinking of both strangers and heavy drinkers known to them, and were asked to rate this level of harm from 1 to 10. They were also asked questions about the types of harm they experienced.Findings: Overall, respondents were fairly consistent in their responses to these two measures, with the mean score of a little or a lot of harm similar for both stranger and known drinker harms. Prediction of the two types of scores was similar, based on the respondents’ experience of harms; however, tangible stranger harm did not predict being harmed a lot.Conclusions: The 1 to 10 score is better predicted by harms experienced; however, this may be due to a lack of variance in the dichotomous question. Equivalence scores are outlined and discussed.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".