Legislative reciprocity: Using a proposal lottery to identify causal effects
Bibliographic record
Abstract
Abstract Although much has been written on legislative reciprocity, rarely have scholars had an opportunity to leverage a randomly assigned asset to assess whether and how legislators reciprocate when their colleagues assist them. Using the lottery that allows Canadian Members of Parliament (MPs) to propose bills or motions, we examine whether MPs’ priority numbers affect their proclivity to second motions made by other MPs, which would be expected if MPs sought to build support for their own proposals by supporting proposals by others. Although MPs almost always make a proposal if their priority number allows them to do so, we find a weak relationship between MPs’ priority numbers and their probability of seconding others’ proposals. Moreover, when we look at successive parliaments, we see only faint indications that those who, by chance, won the right to propose in the previous session (and who therefore were eligible to attract seconds) are more likely to second others’ proposals in the current session. Although subject to a fair amount of statistical uncertainty that will gradually dissipate as future parliaments are examined, this pattern of evidence currently suggests that correlated seconding behavior among legislators is more the product of homophily than reciprocity.
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 | 0.083 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".