A dynamic panel analysis using SIPRI’s extended military expenditure data: The case of Middle Power nations
Why this work is in the frame
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Bibliographic record
Abstract
This study employs SIPRI’s extended military expenditure dataset to estimate a dynamic panel analysis of Middle Powers’ defense posture. The dynamic approach, particularly the Auto Regressive Distributed Lag (ARDL) approach, permits simultaneous, but separate, assessment of short- and long-run effects of a particular variable on military expenditure. We verify the robustness of earlier findings on Middle Power nations’ defense posture. In particular, their military expenditure tends to an income elasticity of greater than one indicating that military power is, at least in part, a status good. In addition, Middle Powers react to threat variables that proxy global instability, such as nuclear power proliferation, and they use foreign aid as a complementary policy tool. Competing demands for funds lead to significant tradeoffs between military and nonmilitary government spending.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it