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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 it