AN ANALYSIS ON REDUCING SPEECH DISORDER OF THE STAMMERING FACED BY PRINCE ALBERT IN “THE KING’S SPEECH’ MOVIE
Bibliographic record
Abstract
Language is a part of human life that without language, people cannot interact each other. They make some conversation, joking etc by using it. So that, they need to have a good speaking, hearing, and understanding while using it. But, sometimes there are some people who have a trouble with their speaking, hearing, or understanding, which make them feel hard to interact with other people, for example like stammer. Stammer is one of speech disorders, which can make people repeat a letter or word of the sentence. It seems like what Prince Albert in The King’s Speech movie who suffered the stammering and tried to reduce it. Therefore, the researcher conducted the research to know some methods used by Prince Albert in reducing his stammer and the result of the methods. This study used descriptive qualitative. The research object of this research was the movie entitled The KingÂ’s Speech. The whole data of this research were taken from dialogues, utterances, and events in the movie. In this study, the writer uses documentation as the instrument of gaining data that he watched the movie many times, so he took some notes from the movie. In The King’s Speech movie, there are 10 methods were found by the researcher, which were used by Prince Albert in reducing his stammer, such as building up the physical organs, which includes loosening jaw, rolling on the ground, and screaming, mental equilibrium, which includes respiration, synchronizing and harmonizing mental and physical actions, which body movement, and some other methods such as listening to music, singing, and pausing. Besides, almost all therapies used by Prince Albert were successful, especially the methods he did with Lionel Logue.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".