Bibliographic record
Abstract
Within the field of Security Studies, strategic culture is no longer the sole domain of major powers as smaller and middle powers are receiving increasing attention and entering the debate on this important phenomenon. Although acknowledged as a difficult topic within the field of Security Studies and its interface with Strategic Studies, theoretical progress on strategic culture nonetheless comprises three distinct waves. These waves are buffeted between ideas on little (if any) scope for change and viewpoints of more scope for change than depicted by earlier thought. Nonetheless, no real consensus exists and the theoretical landscape is characterised by a multitude of different opinions. Although literature on this topic extends into the 21st century, strategic culture remains unexplored in South African academic literature on security and strategy. However, drawing upon third-generation theory, shifts in the practice of South African strategic culture come into focus. These shifts offer some insights into particular South African practices, but these preferences remain vulnerable to the declaratory-operational void posited by second-generation theory. It appears that progress in cultivating or even imposing a different South African strategic culture through changes in political culture, foreign policy and defence, is hampered by the operation of inconsistency and norm stretching.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".