Time and the Fulfillment of Election Pledges
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
In this article, we highlight the importance of accounting for time in the study of pledge fulfillment, effectively adding a significant element to the ongoing academic discussions of the factors that influence the fulfillment of party promises. Unlike previous analyses in which pledge fulfillment is assumed to be a uniform process occurring over time, we analyze party pledge fulfillment using a discrete time approach: doing so highlights yet unobserved dynamics. More precisely, we find that if the government does not enact pledges within the first half of its mandate, the probability of these pledges ever being fulfilled drops drastically. The discrete time modeling approach also allows us to investigate the relationships existing between the budget balance and pledge fulfillment more thoroughly. Our research also extends the study of pledge fulfillment to a new case, the province of Quebec, for the period of 1994–2014 encompassing six governments. Finally, we also conduct similar analyses on Canadian pledge fulfillment data spanning seven successive governments from 1993 to 2015. This study analyzes a total of 1431 manually coded election pledges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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