IMPLEMENTATION OF THE BALANCED SCORECARD MODEL IN LOCAL SELF-GOVERNMENT AUTHORITIES
Bibliographic record
Abstract
Relevance of the study is due to the need to implement tools of strategic municipal management in local self-government authorities in order to successfully complete the reform of decentralization. Foreign experience testifies that one such tool is the Balanced Scorecard (BSC) of Kaplan and Norton, which was evolved from a performance management tool for business to a dominant system of strategic management in the public setor. This tool allows use of non-financial indicators, take into account social aspects and environment as well as manage risks under uncertainty. The article argues that the BSC implementation in local self-government authorities will ensure the solution of the following tasks: 1) aligning the strategic and tactical goals with measures for their implementation; 2) increasing the institutional capacity of authorities (through development and motivation of personnel, formation of innovative organizational culture, introduction of performance management system, improvement of internal processes, development of information systems); 3) increasing the transparency and stakeholders confidence, in particular foreign investors, to attract financing in the post-war period. It was found that the structure of the BSC model and the algorithm of its implementation depend on the field of activity, size, tasks and features of the organization. An algorithm for developing the BSC model of the city council executive committee (its structural unit for the pilot project implementation) is proposed and the main strategies for the four components of this model (stakeholders, finance, internal processes, training and development) are identified. Based on the experience of Canada, Denmark, the Czech Republic and other countries, the main advantages, conditions of success and challenges of implementing the BSC concept in local self-government authorities are identified, taking into account political, organizational, financial and other aspects. It is proposed to define the ways to solve considered challenges using the design thinking methodology.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".