A transformative change through a coordination process and a steering agency. The case of the financial information system of the French central state
Bibliographic record
Abstract
Recent scholarship has focused on how coordination mechanisms are implemented by public sector organizations, thereby paying attention to coordination as a process. This article studies the coordination process that resulted in the implementation of the interministerial financial information system of the French central state—named Chorus. Chorus is a case of an unlikely coordination process rolled out in the non-conducive context of the French Napoleonic Administration. Chorus aimed at connecting all ministries’ administrative services to a shared information system, while ministries were previously using their own systems and applications. Based on the literature on mechanisms of coordination, and focusing on the role of existing institutions and the actors involved in the coordination process, the analysis has two main results. First, AIFE—“Agence pour l’informatique financière de l’État”, the agency in charge of the implementation of Chorus—steered the process by developing a stepwise network-based interministerial strategy. Second, the coordination steered by AIFE resulted in a transformative change of the French state's financial and accounting structures through a layering process of change. Thereby, the article contributes to the empirical analysis of public administrations’ recent changes toward increased coordination at the central level by studying recent reforms in France and their outcomes. Points for practitioners This article shows that coordination processes within public sector organizations are context sensitive and depend on the behavior of the “agents of change” in charge of these processes. In contexts that are non-conducive to transformative change (e.g. siloed structures, presence of veto players), the set-up of agile, resourceful and autonomous change agents is key. When veto players may oppose structural change, the article suggests setting up network-based coordination processes aiming at incremental evolutions inducing transformative change.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".