Dommett, Katharine, Glenn Kefford and Simon Kruschinski. 2024. Data-driven campaigning and political parties—Five advanced democracies compared
Bibliographic record
Abstract
When looking at media coverage and the public discourse it seems as if all election campaigns nowadays are highly data-driven, with political parties being able to individually target voters in order to mobilize or persuade them.In their book, Katharine Dommett, Glenn Kefford and Simon Kruschinski set out to paint a more differentiated picture of data-driven campaigning (DDC) by looking not only into to the so-far dominant case of the USA, but by comparing five advanced democracies: the USA, the UK, Australia, Canada and Germany.They aim to "demystify the practice of DDC, looking beyond the hyperbolic claims and sales pitches to examine how DDC is employed by political parties" and succeed in showing that "not only [...] DDC is by no means uniform, but also that data can be collected, analyzed and deployed in different ways" (p.191).One of the many strengths of the book is its clear structure: The authors start with an introduction (Chap.1) before presenting their theoretical framework (Chap.2).Within this theoretical framework and the analysis built upon it lies the biggest contribution of this book.Building upon an understanding that DDC can come in many different forms and can be used to promote different goals, they divide DDC into four components, namely data, analytics, technology, and personnel.Subsequently, while expanding upon existing research, the authors identify variables that influence these four components on different analytical levels: the party, regulatory, and system level.The following chapters focus on describing the variation found in each of the four DDC components within the five countries, starting with data in chapter three.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".