Application of the CART Model to Classify the Perception of Young Canadian Teenagers on the Effect of Marijuana on Health
Bibliographic record
Abstract
The use of the illicit drug, marijuana has increased over years among young teenagers in different parts of the world and its harm on the health is generally well-known. This paper attempts to study the perception of young adolescents of 13-15 years old residing in Canada towards the danger of marijuana on health. To do so, a classification and regression tree (CART) has been applied on the data from the 2012 National Anti-Drug Strategy (NADS) Youth Advertising Recall and Tracking Survey. The decision tree has been applied and pruned on a training data set (70%) and evaluated on the testing data set (30%). The results show that the main indicators which impact on the perception of a teenager towards the harm marijuana has on health are the perceptions towards psilocybin (another illicit drug), the province in which the teenager lives and whether he/she has been ever offered drugs. The overall error rate on the testing data set based on the confusion matrix is less than 20% and the area under the ROC curve is relatively high showing that the model is accurate in classifying the perception of young teenagers on the health marijuana has on health.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".