Bibliographic record
Abstract
Since the end of the Second World War most modern armies have been conventionally structured and equipped to fight high intensity conflicts against like armed nations. Congruently, there has also been many low intensity conflicts in which similarly equipped nations found themselves engaged. In response to these low intensity conflicts, nations employed the forces available to them, which were generally armor and mechanized in nature. The result of these conflicts have made the relevance of heavy armor, specifically the tank on the asymmetric battlefield a point of contention for the last half century. The question this poses is: How were conventionally equipped, tank heavy forces employed in COIN operations and were they successful? To determine this, examples of French operations in Indo China, the United States' involvement in Vietnam, Somalia, and Iraq, Canadian Afghan operations, and Russia's combat in Chechnya and Afghanistan will be analyzed. The focus for each case study will discuss the situation and threat, tactics used by the counterinsurgency force, modifications to vehicles or doctrine, and the ultimate determination of either success or failure of the tank in the conflict. The results of this study are that the combined arms team provides the commander with a lethal and capable force. The initiative is gained by commanders who seek the non-conventional employment of armor despite the situation or terrain. Task organized units or units that train with different branches enjoy greater success with less friction than units task organized under fire. Lastly, units possessing a more deployable package have a greater initial effect on the battlefield.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.022 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".