Replication Data for: No "I" In Team - Party Defectors Data
Bibliographic record
Abstract
Our investigation sets out to address our overarching research question: why are most Canadian parliamentarians so loyal to their party? As a subset to this, we want to understand the institutional forces fuelling expectations that everyone in Canadian party politics must be a team player. This involves looking into the psychology of group loyalty and team constructs, and what workplace relations can inform us about behaviour in partisan groups. We are curious about the ways that loyalty is conditioned among election candidates, rookie parliamentarians and party veterans and, relatedly, what levers of power a leader has available to command loyalty, and why party loyalty is more impenetrable in some provinces than in others. To answer this question, we have created a dataset of parliamentarians who exited a party caucus in Canada from 1980 to 2021 and who continued sitting either as an Independent and/or joined a different caucus during that Parliament. We identified 349 cases involving 333 politicians who sat after departing their caucus voluntarily or through expulsion between January 1, 1980, and December 31, 2021, some of whom did so more than once. We documented names, year of exit, year of defection (if applicable), years in office, province, gender, age and the political parties involved. We cross-referenced our list with an existing database of Canadian party switchers. Each was coded and augmented by a synthesis of over 3,000 news stories into 333 briefing notes about the controversial behaviour of every party leaver we identified from 1980 to 2021.
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 | 0.027 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.096 | 0.029 |
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".