NATO ve EURO Bölgesindeki Savunma Sanayilerinin İktisadi Etkinliği ve Toplam Faktör Verimliliği
Bibliographic record
Abstract
The main purpose of this study is to analyze the efficiency and total factor productivity (MTFP) of the 12 defense industries in NATO and the EUROZONE with the data of the 2013-2017 period. GDP, defense expenditures, import for the defense industry and logistics performance index were used as input variables; while total sales and export values of defense industry were used as output variables in accordance with the data acquired from World Bank (WB) and SIPRI. Static DEA and MTFP were applied to data. According to findings of the CCR models; the USA, UK, France, Germany, Spain, and Netherland were observed as efficient DMUs in all years; whereas the other six countries were inefficient ones. Additionally, according to BCC model, only Turkey and Canada were observed as inefficient ones for five years. MTFP analysis revealed that Turkey and Germany were the two countries experiencing TFP in all periods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".