Data-driven campaigning and democratic disruption: Evidence from six advanced democracies
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
Data-driven campaigning has become one of the key foci for academic and non-academic audiences interested in political communication. Widely seen to have transformed political practice, it is often argued that data-driven campaigning is a force of significant democratic disruption because it contributes to a fragmentation of political discourse, undermines prevailing systems of electoral accountability and subverts ‘free’ and ‘fair’ elections. In this article, we present one of the very first cross-national analyses of data-driven campaigning by political parties. Drawing on empirical research conducted by experts in six advanced democracies, we show that the data-driven campaign practices seen to threaten democracy are often not manifest in party campaigns. Instead, we see a set of practices that build on pre-existing techniques and which are far less sophisticated than is often assumed. Indeed, we present evidence that most political parties lack the capacity to execute the hyper-intensive practices often associated with data-driven campaigning. Hence, while there is reason to remain alert to the challenges data-driven campaigning produces for democratic norms, we argue that this practice is not inherently disruptive, but rather exemplifies the evolving nature of political campaigning in the 21st century.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| 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