The Building Blocks of Defensive and Accusatory Language in Canadian Question Period
Bibliographic record
Abstract
The idea that there are grammatical structures which form accusations and defenses in language has been explored in the context of isolated instances of political debate (Rasiah 2009). This paper goes beyond that, looking at the specific linguistic strategies that compose such a structure, and evaluating those strategies over time. A discourse analysis is used to isolate, contrast and compare argumentative strategies in two different sections from the Canadian Hansard corpus. The first section consists of transcriptions of question period recorded in 2005 while the second is from 2014, allowing for a comparison that explores these trends through time. The strategies found in each section consist of specific linguistic elements which are relevant in the context of grammar structure analysis. Beyond this the individual strategies can also be sorted into larger groups, such as temporal distancing and diverting agentivity, which map the grammar of evasion on a more general scale. These groups expose language trends in political debate, and allow for an analysis of general evasion tactics used in Canadian government. By exploring the implications of said trends, this paper raises the question of political integrity in our Country’s leadership. A presentation of this thesis would explore the specific strategies, however would focus on the general groups, the trends that they expose and their implications. This information could be found relevant in many academic contexts including sociolinguistics, applied linguistics, politics, and English studies.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".