Bibliographic record
Abstract
Experimental research by political scientists on elites has grown dramatically in recent years. Experimenting on and with elites raises important questions, both practical and ethical. Elites are busy people, doing important work under public scrutiny. Therefore, any experiments that use up political elites’ time, risk impairing their ability to do their jobs as well as possible, or put at risk the larger research community’s access to elites should be avoided. Nevertheless, despite these risks and challenges, we argue experimenting with elites has enough benefits both to the research community and to elites themselves, that it should still be done. The relevant question then becomes how should we think about doing experiments with political elites? We propose a framework of value-added and transparent experiments. Our framework is guided by the following two simple rules: Elite subjects should individually benefit from the process of doing the experiment. It should add value to their role as representatives. Second, the identity of the researchers and purposes of the experiment should be transparent. As we argue, these two combined features can still accommodate a large range of experiments, can creatively spark researchers to think up new designs and can protect access to elites for future research. We review two such examples at the end of this essay.
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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 | 0.316 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".