Online political adverts: The effect of disclosures and opportunities for clandestine campaigning
Bibliographic record
Abstract
Abstract The use of digital technology has become an increasingly prominent feature of election campaigns. While many of those using online tools are familiar partisan actors, many others are not. As concerns about electoral transparency have grown, policy makers have moved to implement regulation designed to help citizens recognize the identity of campaigners. In this paper, we test the rationale behind such regulations by asking how disclosures on online adverts—both informal badging and formal imprints—affect evaluation by UK voters. Using experimental survey design, we test the reactions of participants to real Facebook adverts, labeled as originating from both partisan and apparently non‐partisan sources. Across three experiments, we consistently find evidence to support concerns about what we term “clandestine campaigning”; a phenomenon whereby apparently non‐partisan groups can receive a more favorable reception for incongruous partisan advert content than overtly incongruous partisan‐badged campaign material.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".