Unmasking Accountability: Judging Performance in an Interdependent World
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
As local conditions come to reflect extralocal forces, signals of government competence grow more obscure. Yet we know relatively little about how voters evaluate incumbent performance in the context of interdependence. We use a series of simulated voting tasks to examine three theoretical possibilities: blind retrospection, rational discounting, and benchmarking. Across five experiments requiring “voters” to judge performance in a setting that obscures incumbent competence, we find consistent evidence of benchmarking—subjects rewarded incumbents, capable or otherwise, who outperformed a peer. Benchmarking was evident in information processing, information seeking, and both hard and easy tasks. The disposition to benchmark was also generally robust to the availability of information that clarified incumbent competence. Our findings advance the study of performance voting, especially its underlying mechanisms, and raise questions about the availability of performance information across domains of government action.
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 | 0.002 | 0.000 |
| 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