Accountability in Time: Evolution and Expertise in Participatory Institutions
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
abstract: How do participatory institutions change over time? Previous research has focused on exogenous changes, such as legal reform or leadership replacement. But institutions also evolve endogenously, through processes of behavioral and compositional change on the part of citizen claimants and government officials. These processes can gradually reshape institutions to become more responsive to either expert or nonexpert claimants. The authors refer to such processes as brokered and grassroots models of social accountability. In the context of Mexico’s access-to-information system, the authors employ new machine-learning-generated measures to analyze nearly two million information requests and responses filed between 2003 and 2019. They find evidence that shows claimants becoming more sophisticated over time, and officials becoming more responsive to these expert claimants—both findings consistent with a brokered accountability model. Quantitative and qualitative evidence reveals mechanisms of behavioral and compositional change by citizen claimants and government agents.
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 | 0.001 | 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 | 0.000 | 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