AN ANALYSIS OF ENGLISH SLANG WORDS DISCUSSED BY SLANG CONTENT CREATORS ON TIKTOK
Bibliographic record
Abstract
This study discusses the analysis of the meaning and types of English slang words used by English speaking people on TikTok. This study also discusses the contribution of English slang words in language learning. There are 50 data that have been analyzed by researchers. The methodology of this research was qualitative research. The study consisted of three steps, they were; reading, selecting, and classifying the data. To identify the types of slang words, the researcher used the theory of Michael Munro (2007). The researcher also used online and manual to find the meaning of English slang words. Researchers found six out of eight types of slang words on TikTok. They are United States slang, Canadian slang, Australian slang, New Zealand slang, South African slang, and Irish slang. The researcher did not find slang words that belong to the type of Caribbean slang and South Asian slang. There are 16 United States slang words, 3 Canadian slang words, 11 Australian slang words, 10 New Zealand slang words, 5 South African slang words, and 5 Irish slang words. The United States slang type dominates the type of slang used by English speaking people on TikTok.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".