New Fighter Aircraft Acquisitions in<scp>B</scp>razil and<scp>I</scp>ndia: Why Not Buy<scp>A</scp>merican?
Bibliographic record
Abstract
How do states decide where to source arms? Drawing on theories of international relations, we consider the recent fighter aircraft competitions in Brazil and India, and analyze why the U.S.‐made aircraft lost to their European rivals. Official statements offered by the Brazilian and Indian governments have cited inferior aircraft performance, technology‐sharing issues, and prices. These explanations may be valid, but their main purpose is to direct attention away from the fact that military procurement is, above all, a matter of international politics and policy. Using analytical eclecticism as our guide, we selectively combine constructivist, liberal, and realist theoretical elements to provide a more comprehensive explanation of why Lockheed Martin and Boeing failed to sell fighters to Brazil and India. Related Articles Catalinac , Amy L . 2007 . “.” Politics & Policy 35 (): 58 – 100 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2007.00049.x/abstract Quinn , Adam . 2007 . “.” Politics & Policy 35 (): 522 – 547 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2007.00071.x/abstract Rosen , Amanda M . 2015 . “.” Politics & Policy 43 (): 30 – 58 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12105/abstract Related Media So , Vishnu . 2012 . “.”NDTV. December 8. Duration: 16 min, 42 sec. http://www.ndtv.com/video/player/bigger-higher-faster/the-story-of-the-rafale/257581 . 2015 . “.” Notícias Militares. January 10. Duration: 3 min, 38 sec (in Brazilian Portuguese). https://www.youtube.com/watch?v=8P12stwG1jA
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".