Risk Perception, Accounting, and Resilience in Public Sector Organizations: A Case Study Analysis
Bibliographic record
Abstract
There are various factors that can affect an organization’s ability to overcome a crisis and the uncertainties that arise thereafter. Little is known about the process of organizational resilience and the factors that can help or prevent it. In this paper, we analyzed how public sector organizations build resilience/traits of risks awareness, and in doing that, we derived some elements that could affect the process of resilience. In particular, drawing on the conceptual framework proposed by Mallak we analyzed an in-depth case study in a public sector organization (PSO) identifying some contextual dimensions implicated in the process of building resilience. In our analysis, we identified two main elements that affect resilience: Risk perception and the use of accounting. Results shown how risk perception is perceived as a trigger, while accounting is considered as an enforcer in the process of building resilience capacity. The results also show the way accounting is implicated in the management of austerity programs and supporting the creation of a resilient public sector organization. In our case, the risk has become an opportunity for change. In the face of these budget cuts, management began refocusing the company’s mission from infrastructure maintenance to providing services with a market-based logic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".