Ready, willing, <i>and able</i>? Bureaucratic capacity, slack resources, and political control
Bibliographic record
Abstract
Abstract Recent research suggests that bureaucratic responsiveness to political preferences may depend as much on organizational capacity as it does on incentive alignment, information recovery, and the strategic interaction of principal and agent. Better-resourced bureaucracies should be more able to comply with new political directions, irrespective of their willingness to do so. But because so much bureaucratic capacity is sunk into implementing the prior policy commitments of current and former principals, responding to new political signals will depend—much more specifically—on agents possessing adequate slack resources. This spare capacity should aid signal detection and program development; decrease hesitance at over-committing to new assignments in volatile environments; and provide resources for implementing changes whilst maintaining prior commitments. Using two-way fixed-effects regression and a novel dataset of 1,430 legislative requests of the UK executive, we confirm that possession of slack resources specifically (rather than organizational capacity generally) significantly increases the likelihood of bureaucracies consenting to make program changes requested by parliament. Agents with slack also commit to more precise timelines for implementation. And survival analysis further reveals that, once committed, bureaucracies with more budgetary slack complete their assignments more expeditiously.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".