Bibliographic record
Abstract
The public economy, which has reached a significant size in terms of both quality and quantity, collects and spends resources between 30% and 70% of GDP on average, shapes up the economy, and acts as a complementary element next to the private sector. Therefore in this study, the relationship between the size of the public sector (its spending) and its efficiency was analyzed. For this purpose, a 24-year data of the period, 1995 to 2018, were evaluated. In this context, a comparison was made between the data of 36 OECD-member countries and seven non-OECD countries. In conclusion, it was seen that, since OECD-member countries are generally high-income countries, public expenditures in those countries are far from populism and are used effectively in order to meet the social needs. On the other hand, in developing countries, since the growth in the public expenditures tends towards mostly public goods and services in which political actors are given priority, the efficiency either remains limited or is negative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".