The application of Mcenery`s classification of bad language words in the Raid Redemption and The Raid 2 : ``Berandal`` Movies
Bibliographic record
Abstract
This paper aimed to find out how McEnery's classification of bad language word was applied in The Raid Redemption and The Raid 2: "Berandal" movies.The first movie soared through the world after being nominated in a Movie Festival in Toronto.The following sequel was also very popular.It expanded the action into prolonged time span and also received positive criticism from the audiences.In these movies, there were a lot of occurrences of Indonesian swear word and uniquely many of those occurrences could fit into McEnery's classification of swear word.Consequently the research problem was formulated as how McEnery's classification of bad language words can be applied in categorizing the occurrences of swearing in the Indonesian language especially in the The Raid Redemption and The Raid 2: Berandal.?This research employed content analysis.The research' object was the theory of Bad Language Word classification proposed by McEnery in 2007.The data in which the main theory would be applied were two well-known Indonesian movies namely The Raid Redemption and The Raid 2: Berandal.Considering that the main discussion was about the bad language that occurred during the movie then the data displayed and discussed in this paper would only be the context and the occurrence of the bad language word itself in form of dialog.The researcher used references from books, online websites, general knowledge, films, and dictionaries to support this study to reveal how McEnery's classification of bad language word is applied.Based on the analysis, the researcher found that only seven out of sixteen categories in a categorization that was proposed by McEnery could fit into the movies.The researcher found that it is important to separate the classifications into subgroups to overcome overlapping problem.Seven categories that occur in both movies are cursing expletive, general expletive, idiomatic set phrase', literal usage denoting taboo referent, pronominal' form with undefined referent, imagery based on literal meaning and figurative extension of literal meaning..
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".