Formulaic Forms of Address as (Im)politeness Markers in Prime Minister’s Questions: Margaret Thatcher Versus Theresa May
Bibliographic record
Abstract
Prime Minister’s Question Time (PMQs) is a political discourse genre with a long and distinguished history. Framed by formulaic forms of address, the exchanges follow a set of turn-taking “rules” that has evolved over time. The study presented here examines the (non)use of honorifics and other polite forms intrinsic to PMQs during interactions between two female Prime Ministers and their respective Leaders of the Opposition: Margaret Thatcher and Neil Kinnock, Theresa May and Jeremy Corbyn. From diachronic and gendered perspectives, the study implements a mixed methods framework to address the following research questions: 1) has the use of formal politeness markers decreased over time? 2) Do gender dynamics influence impoliteness strategies in the context of PMQs? 3) In the shift from verbal to written discourse, what diamesic transformations appear in the official parliamentary transcriptions? The self-built corpus includes selected video recordings of PMQs from each of the Prime Ministers’ mandates, and the corresponding official transcripts published online by Hansard. The audiovisual texts were viewed and examined, the speech was manually transcribed, and then compared to Hansard’s version. Initial findings suggest that over time, across genders, and in Hansard’s digital transcripts, the use of politeness forms in PMQ exchanges appears to be diminishing as formulaic expressions are omitted or substituted with pronouns.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| 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".