Deixis Found in King Charles III's Speech from the Throne (2025)
Bibliographic record
Abstract
This study examines the use of deixis in King Charles III's Speech from the Throne (2025) by applying Yule’s (1996) framework, which categorizes deixis into person, spatial, and temporal types. Using a qualitative descriptive approach, the analysis focuses on the official transcript published on the Government of Canada’s website. The data were categorized and analyzed to determine the frequency and purpose of each deictic category within the speech. A total of 127 deictic expressions were found, consisting of 66 person deixis (51.97%), 55 spatial deixis (43.31%), and 6 temporal deixis (4.72%). Person deixis, particularly pronouns such as I, we, our, and you, is the most dominant and serves to build solidarity and shared identity between the King and his audience. Spatial deixis, including here, there, this, and that, reinforces unity by referring to physical and symbolic aspects of the nation. Although temporal deixis appears infrequently, it connects the present moment with Canada’s historical continuity and future aspirations. Overall, deixis functions as a strategic linguistic tool that strengthens authority, unity, and national identity in royal political discourse. Keywords : Deixis; Political discourse; King Charles III's Speech; National identity
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".