Bibliographic record
Abstract
Purpose The purpose of this paper is to study vaporizers – especially the vape pen – as a new technology in cannabis use. Until now, almost all information on the use of vaporizers or e-cigarettes for cannabis consumption has come from the internet, the popular press, and accounts by users, but not from the scientific literature. More research is needed. Design/methodology/approach Since scientific studies of the phenomenon are virtually non-existent, the author will also base his study on sociological reflections upon internet sites and articles published both in subcultural and mainstream media. The author will document a national estimate of the prevalence of vaping based on a recent population survey in Finland. Findings Vaping is an emerging trend in cannabis culture internationally. It has been seen as a healthier route of administration than traditional ways of smoking cannabis. Other images, created especially with the help of advanced high-tech machinery and stylish and fashionable designs for the vape pen, are aiming at being cool and easy to use. In Finland, 6 percent of cannabis users make regular use of a vaporizer, and around a quarter of users use one occasionally. A vape pen or e-cigarette was regularly used by 2.6 percent and occasionally by 9.1 percent of cannabis users. Originality/value The trend of increasing vaping and the use of new devices has not been properly recognized among researchers. The paper presents some original results from a national population survey.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".