Marketing Responsible Drinking Effectively to Young Adults
Bibliographic record
Abstract
The primary goal of this research is to identify and examine the components of responsible drinking advertisements. We will examine industry and government related advertisements as we try to understand one of our major questions: does the source influence the validity of the message? The next group of major questions that we will be looking to answer is how are the vague quantifiers used in responsible drinking campaigns interpreted by the public? How many drinks do people consider “too much?” What does “drink responsibly” really mean? The third major question is whether or not an individual’s current consumption patterns of alcohol have any effect on how individuals assess responsible drinking campaigns. Our qualitative research has indicated that social influences can be strongly related with drinking patterns; this will be further examined in our quantitative research. Also, we will be looking into some of the psychology behind industry and government sponsored advertisements as well as gathering and interpreting information from a sample of our target demographic. Our target demographic consists of both male and females between the ages 18-24. Our literature review and qualitative analysis gave us good insight into some of the potential answers to our questions. We will use these potential answers from our previous research to guide us as we attempt to conduct conclusive research based on a sample data of 169 individuals. Our findings will aid us in developing conclusions and recommendations for Alberta Health Services.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".