Bibliographic record
Abstract
Linguistic diversity in drug slang) The consumption of illicit substances is a characteristic phenomenon of today's society. Industrial and technological development has favored the spread of drugs throughout the world, through new ways. Making its distribution a crime, which is punishable according to the criminal code in force in all countries of the world, dealers and drug addicts show once again the ingenuity and originality of human beings when creating a code language. In this article we intend to identify and analyze from a semantic point of view the words belonging to the drug slang. In recent decades, the slang language of the drug has been incorporating several words belonging to various semantic fields. This is due, in the first place, to the need of the supplier and the buyer of those substances to create or search within the language for a word that helps them to name a concept in a cryptic way. In the first part of the article we will talk about the main characteristics of drug slang, then we will classify the words found in thematic areas and therefore we will see what are the procedures used by the creators of drug slang when searching for it a metaphorical synonym to the original name of the drug.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".