Temporary Disenfranchisement Revisited: A Report from the 2023 Montréal Replication Games on the Robustness of Recent Findings in the APSR
Bibliographic record
Abstract
Leininger et al. (2023) study the political consequences of temporary disenfranchisement. Taking advantage of differentiated voting elegibility thresholds applying in different elections in Germany, they analyze how first-time voters react when losing eligibility in a follow-up election. They exploit this setting in a difference-in-differences design using panel data. They find that temporary disenfranchisement decreases perceived external efficacy by 0.19 points on a five-point Likert scale and satisfaction with democracy by 0.14 points. Both results are statistically significant at the five-percent level. In contrast, internal efficacy and political interest remain unaffected by the treatment, and regaining voting eligibility is not associated with statistically significant changes in respondents' attitudes. This report focuses on the computational reproducibility and robustness replicability of these findings. To assess the paper's reproducibility, we first attempt to reproduce the paper's estimates and figures using the author's replication materials. In a second step, we perform several robustness checks by means of alternative difference-in-differences specifications using coarsened exact matching and entropy balancing, and a closer examination of panel attrition. Overall, we find complete reproducibility of the original replication materials. Our robustness checks confirm the sign congruence and significance of coefficients reported in the original paper. We raise the issue of potential bias due to differential panel attrition rates between treated and untreated respondents.
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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 | 0.068 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".