The Preferred Terminology Implemented by Psychedelic Users Existing on Online Platforms: A Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: The epidemiologic magnitude of the NPS (ab)use has been thoroughly explored and geographically mapped in the developed world. However, there are still untapped geographic locations primarily in the developing countries including the Middle East. Historically, mapping has been done via observational analytics, cross-sectionally and longitudinally, in addition to few experimental studies.MATERIALS & METHODS: The study is cross-sectional; it will implement the internet snapshot technique, in addition to the application of thematic analysis and psychoanalysis of comments of NPS (ab)users on the online drug fora and social communication media. The data collected will be analysed for the purpose of concluding a statistical inference in relation to the terms most preferred by substance (ab)users towards hallucinogenic substances.RESULTS: The population was dominated by right-handed males; most of which were either heterosexual or bisexual. Psychedelics users have a preference for specific terms; psychedelic (73%), entheogen (12%), hallucinogen (11%), spiritual aids (3%), mysticomimetic (1%), psychotomimetic (<1%), medicines (<1%), and trip (<1%). Right-handed users who used the term psychedelic were a majority (53%), while right-handed individuals using the terms entheogen and hallucinogen contributed to 8% for each.CONCLUSION: This study is in line with other studies confirming the minute contribution of the Middle East to the global NPS phenomenon. Additional studies are mandatory for different populations including; students, academics and researchers, medical professionals, psychiatric patients, military and para-military organisations, delinquent and prisoners, and even terrorists.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".