Attitude Analysis of Michelle Obama’s Speech on the Opening Day of the Democratic National Convention in Philadelphia in 2016
Bibliographic record
Abstract
This study aims to analyze Michelle Obama’s speech on the opening day of the Democratic National Convention in Philadelphia in 2016 using the appraisal system. The data were obtained from the internet using the document method. Qualitative and descriptive approaches were undertaken to achieve the desired objectives. The results show that Michelle applied all the positive judgment tools in her speech to show a positive attitude toward Hillary (i.e., 22% normality, 50% capacity, 9% tenacity, 7% veracity, and 10% propriety). Conversely, Michelle applied negative judgments in her speech (i.e., 12% normality, 12% capacity, and 75% propriety); thus, Michelle did not apply tenacity and veracity while implicitly referring to Donald Trump. Michelle demonstrates that she is a skilled public speaker who can articulate her point of view clearly and persuasively. Her words reveal her thoughts and feelings about the future of her country and the upcoming presidential election. In future studies, other discourse semantic systems should be considered to analyze Michelle Obama’s speeches.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".