A Gladiatorial Arena: Incivility in the Canadian House of Commons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In parliamentary systems, it is common for legislatures to offer a regular opportunity for their members to question government ministers. While these institutions fulfill an essential function for democratic accountability, they also represent an occasion for incivility to creep into political discourse. This article investigates the incidence of uncivil behavior in these institutions and identifies some of its covariates. Our focus is on the Canadian House of Commons. Using cutting-edge, open-source machine learning models, we measure the incidence and evolution of incivility in Question Periods from April 2006 to June 2021. We find significant evidence of uncivil behavior, especially insults and toxicity. Through a multivariate regression analysis, we show that variations in the incidence of uncivil behavior over time and across members of various parties are correlated with the time remaining until the next general election, the institutional roles of parties, the balance of power, and the language of interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it