Data-driven campaigning and democratic disruption: Evidence from six advanced democracies
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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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".