Help Me Help You: Conducting Field Experiments with Political Elites
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
Field experiments can teach important facts about the political world to both political scientists and political elites, whose shared interest in how that world actually works should encourage greater collaboration between the two. Nonetheless, for political scientists, conducting field experiments with elites can seem prohibitively challenging. Drawing on four field experiments with political elites, two in Benin and two in Canada, we outline key lessons on negotiating and conducting field experiments with political elites. Specifically, we outline how ethical concerns can be addressed and overcome. We discuss how the expectations and timelines of campaigns and elites can be managed, particularly when they (appear to conflict) with academic timing and process. Finally, we outline several general concerns about implementation of field experiments and provide some unique solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.021 |
| 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