The Fulfillment of Parties’ Election Pledges: A Comparative Study on the Impact of Power Sharing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Why are some parties more likely than others to keep the promises they made during previous election campaigns? This study provides the first large‐scale comparative analysis of pledge fulfillment with common definitions. We study the fulfillment of over 20,000 pledges made in 57 election campaigns in 12 countries, and our findings challenge the common view of parties as promise breakers. Many parties that enter government executives are highly likely to fulfill their pledges, and significantly more so than parties that do not enter government executives. We explain variation in the fulfillment of governing parties’ pledges by the extent to which parties share power in government. Parties in single‐party executives, both with and without legislative majorities, have the highest fulfillment rates. Within coalition governments, the likelihood of pledge fulfillment is highest when the party receives the chief executive post and when another governing party made a similar pledge.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it