Replication Data for: A Gladiatorial Arena: Incivility in the Canadian House of Commons
Bibliographic record
Abstract
Here is a list of the files contained in the replication files, along with a short description: Code Note: The scripts contain dependencies, so they should be run in the order they are listed. 1. Transcripts.R: This file contains the code to upload and format the Question Period transcripts from the House of Commons website. 2. Transcripts_Date.R: This file contains the code to upload and extract the data of meetings from the House of Commons website. 3. Perspective.R: This file contains the code to process the Question Period transcripts through the Perspective API. 4. Analysis 1.R: This file contains the code to replicate the analysis presented in the body of the paper and some of the elements in the Online Appendix. 5. Analysis 2.R: This file contains the code to replicate the analysis of the Question Period transcripts in French reported in the Online Appendix. 6. Analysis 3.R: This file contains the code to replicate Figures G2 to G5 in the Online Appendix. Data Note: These files are generated using the previous scripts. They are enclosed in replication files for comparison purposes. 1. df_Date.Rdata 2. df_English.Rdata 3. df_French.Rdata 4. df_Weekly_English.Rdata 5. df_Weekly_French.Rdata 6. df_Weekly_Language_English.Rdata 7. df_Weekly_Language_French.Rdata 8. prsp_English.Rdata 9. prsp_French.Rdata
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.
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 | 0.011 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.373 | 0.116 |
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".