A Corpus-based Analysis of King Charles’s Inaugural Speech from the Perspective of Transitivity
Bibliographic record
Abstract
After taking the throne, King Charles III addressed a pre-recorded inaugural speech to the people of the United Kingdom and all Commonwealth countries. In this historic speech, the new king affirmed his role as per the customs of the United Kingdom, deeply mourned his mother, and acknowledged his family's new roles by providing them with their respective titles. This study used Halliday's Systemic Functional Grammar theory to conduct a transitivity analysis of King Charles III's inaugural speech with the aim of outlining the process types, participants, and circumstances of the speech and identifying the most dominant transitivity type process. A corpus-based analysis was conducted to achieve these aims and UAM software was used to analyze the inaugural speech. The analysis revealed that the frequent transitivity processes found in the speech were material, mental, relational, and verbal processes, and each process type involved its own particular participants and different circumstances. Most significantly, material clauses were the most dominant in the speech to affirm the king's determination and seriousness as he took on his new kingship duties and to ensure the continuity of serving the people of the United Kingdom and the Commonwealth nations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".