Greek Public Sector’s Efficient Resource Allocation: Key Findings and Policy Management
Bibliographic record
Abstract
The public sector has limited resources, and how these resources are allocated in expenditures and investments is crucial. Our article focuses on controlling this allocation for the Greek economy from 2000 to 2021, which includes the country’s debt crisis. To do so, we utilized data from national accounts, categorized inputs and outputs, and examined their volatility and stability over time using statistical and mathematical methods. Our analysis revealed that the crisis impacted the size and allocation of public inputs and outputs. While some sectors of the Greek economy displayed stability in financing over time, others were more volatile. Using a mathematical accounting approach contributes to the academic discourse on rational resource allocation in the public sector. Our results validate the tax hypothesis for primary revenues and expenditures and advocate that it is necessary to make targeted recruitments in the sector that is needed each time while keeping the total number constant, which leads to the need to redistribute public sector workers. In the same way, public projects should not only focus on infrastructure projects but should also be spread to new areas related to climate change and the agricultural sector.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".