Media Attention and Bureaucratic Responsiveness
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 does media attention shape bureaucratic behavior? We answer this question using novel data from the Mexican federal government. We first develop a new indicator for periods of anomalously heightened media attention, based on 150,000 news articles pertaining to 22 Mexican government ministries and agencies, and qualitatively categorize their themes. We then evaluate government responsiveness using administrative data on roughly 500,000 requests for government information over a 10-year period, with their associated responses. A panel fixed-effects approach demonstrates effects of media attention on the volume of outgoing weekly responses, while a second approach finds effects on the “queue” of information requests already filed when anomalous media attention begins. Consistent across these empirical approaches, we find that media attention shapes bureaucratic behavior. Positive or neutral attention is associated with reduced responsiveness, while the effects of negative attention vary, with attention to government failures leading to increased responsiveness but attention to corruption leading to reduced responsiveness. These patterns are consistent with mechanisms of reputation management, disclosure threat, and workload burden, but inconsistent with mechanisms of credit claiming or blame avoidance.
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 | 0.009 | 0.011 |
| 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 | 0.001 |
| 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