Middle Power’s Soft Engagement: Whether Hard Power Can Be a Tool of Soft Power?
Bibliographic record
Abstract
This article is an attempt to shed light on all possible aspects of military soft power with reference to the case study of the Pakistani military. This study reveals the Pakistani state’s effective use of the military (hard power) as a soft power, which is the only available tool at its disposal due to its inability to use other soft power tools. Without such effective use of hard power as soft power, the Pakistani state would not have had such an impact on many countries of the world, especially the Muslim world and the Middle East in particular. Therefore, can the Pakistani influence be measured by the Pakistani flags all over Baku after the second Nagorno-Karabakh war, the economic help from the Gulf countries, the appraisal statements from Sri Lanka and Bosnia, the peacekeeping missions in many countries, and so on? Was this all possible because of Pakistan’s military soft power? Is it the latter that has saved the Pakistani state from a total economic collapse? This paper expands Nye’s soft power theory to the middle power’s use of hard power as a soft power using the qualitative and quantitative methods of analysis. Moreover, it is of paramount importance to look into the possibility where hard power tools can be used as soft power (direct or indirect military actions) when it is the only option available due to the state’s inability to use other soft power tools.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.005 |
| 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".