Sociopragmatic Analysis On Expressive Utterances Showed By Social Media Users In Responding The Colorblind Men Seeing Color
Bibliographic record
Abstract
This study aims to analyze the intentions of expressive utterances and politeness strategy showed by social media users in responding the colorblind men seeing color. This study is descriptive qualitative study. The data of the study are 61 expressive utterances in the comment column of The Epoch Times Canada Facebook account. The data source of this study is taken from in the comment column of The Epoch Times Canada Facebook account with the video colorblind men seeing color. In collecting the data, the researcher uses documentation and observation. The data is analyzed by using the theory of expressive utterance by Yule (1996) and the theory of politeness strategy by Brown and Levinson (1987). The result shows that there are 11 data (18, 03%) of pleasure expression, 17 data (27,87%) of joy expression, 15 data (24,59%) of like expression, 11 data (18,03%) of dislike expression, 4 data (6,56%) of pain expression, 3 data (4,92%) of sorrow expression. Expression of joy is the most dominant intentions in expressive utterances used by social media users in responding the colorblind men seeing color. This happened because the social media users liked to show joy expression. They have great happiness with this event. Next, the researcher finds that there are 44 data (72, 13%) of positive politeness strategy, 10 data (16, 39%) of negative politeness, and 7 data (11, 48%) of bald-on record strategy. The research does not find the bald-off record given by social media users in responding colorblind men seeing color. Positive politeness is the most dominant strategy of politeness used by social media users in responding the colorblind men seeing color. By using positive politeness, the users exaggerate their interest in responding the colorblind men seeing color for the miracle of the glasses with this rare phenomenon.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".