Media Attention and Bureaucratic Responsiveness
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.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".