Replication Data for: Media Coverage of Campaign Promises Throughout the Electoral Cycle
Bibliographic record
Abstract
Previous studies conclude that governments fulfill a large share of their campaign pledges. However, only a minority of voters believe that politicians try to keep their promises, and many voters struggle to recall the fulfillment or breaking of salient campaign pledges accurately. I argue that this disparity between the public perception and empirical evidence is influenced by the information voters receive throughout the electoral cycle. I expect that the media extensively inform readers about political promises. However, I posit that news outlets focus more on broken than on fulfilled promises and that the focus on broken promises has increased over time. I find strong support for these expectations based on a new text corpus of over 430,000 statements on political commitments published between 1979 and 2017 in 22 newspapers during 33 electoral cycles in Australia, Canada, Ireland, and the United Kingdom. Newspapers inform voters regularly about announced, broken, and fulfilled promises. Yet, across the four countries, newspapers report at least twice as much on broken than on fulfilled promises. Moreover, this negativity bias in reports on political promises has increased substantively. The results have implications for studying campaign promises, negative information in mass media, and the linkages between voters and parties.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.062 |
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".