Reproduction of "Can’t We All Just Get Along? How Women MPs Can Ameliorate Affective Polarization in Western Publics"
Bibliographic record
Abstract
Reproduction report here: https://osf.io/69px3/ Replicators' code: https://osf.io/69px3/ Authors' response: "Thank you for replicating our paper Can’t We All Just Get Along? How Women MPs Can Ameliorate Affective Polarization in Western Publics (APSR 2023) as part of the Montreal Replication Games. We appreciate the attention to detail and rigor applied to the replication project. We are pleased that our initial results replicate well. We appreciate your robust approach to testing the stability of our findings using a country and year ’leave-one-out’ cross-validation strategy. We also thank you for catching the coding error which dropped a handful of cases from the original analysis; we are glad that the results remain substantively the same when this error is corrected. We also are interested in the results from the extension you undertook, finding that our results are primarily driven by left-wing parties’ supporters, in particular parties from the green, radical left and social Democratic parties. On the other hand, the point estimates are positive for all parties excepting the conservative and radical right parties, which can be expected to have the most conservative views on gender roles. We note that the authors’ interpretation, that “the portion of women MPs affects the attitudes of left-wing voters and not the attitudes of the voters most likely to undermine democracy” is true, but that the results also suggest that far-right parties, who most aggressively challenge liberal democratic norms, may be able to “soften” their image among left-wing voters by running female candidates. This is consistent with the argument made by Catalano Week et al (2023), that radical right parties strategically run women to broaden their appeal. Again, we deeply appreciate your replication and insightful extension of our research." Authors' code: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/AHQRVR
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.026 |
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; both teacher heads agree on what is shown here.
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".