The Black Box of Bureaucracy: Interrogating Accountability in the Public Service
Bibliographic record
Abstract
Bolstering accountability among civil servants has been at the centre of public governance reform efforts for well beyond the past decade. A critical gap has been the lack of empirical understanding of the actual accountability practices, especially below the deputy minister level. This article presents initial findings from a larger research study comparing Canada, Australia and the Netherlands aimed at addressing this gap. The study seeks to understand both how, and for what, individual executive, managerial and working‐level public servants are held to account. The research tests an adapted version of Aucoin and Heintzman's and Bovens, Schillemans and 't Hart's respective frameworks on the purposes of accountability. The results suggest that while there is evidence that all four normative purposes of accountability examined – democratic control, assurance, learning and results – are reflected in the actual practice of accountability, practice is wanting in some respect with regard to each of the four.
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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 | 0.043 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.081 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".