The Soldiers Welfare: The Military Keynesianism Perspective for The Indonesian Armed Forces’s Professionalism
Bibliographic record
Abstract
Building professional soldiers into the wishes and commitments of many countries, this is due to the strengthening of threats that are not only traditional threats, but also non-traditional threats. This situation confirms that the presence of professional soldiers will ensure the sovereignty of the state, because the military institution can focus on its duties and functions on the defense of the country. In this context, the Indonesian Armed Forces (Tentara Nasional Indonesia—TNI) is also faced with situations that place TNI institutions to become professionals, relying on democratic civilian government through the defense ministry with an emphasis on improving the welfare of soldiers simultaneously with efforts to modernize Indonesia's defense system. Because the Military-Keynesianism approach believes that improving the welfare of the army is part of the consequences of increasing defense budgets. The paper argues that the increase in defense budget will be correlated with the welfare of the army, although the policy is not directly for the welfare of the army. The article also argues that the increase in the defense budget should improve the TNI foundation as an ideal institution by emphasizing the welfare of soldiers.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".