Bibliographic record
Abstract
Pervitin is a drug developed in Nazi Germany by the pharmaceutical company Temmler, before the start of World War II. Originally sold without prescription to the population, it was claimed to suppress fatigue, make one more alert, reduce hunger, and help fight depression. The main ingredient of this wonder drug was methamphetamine, the primary component of what we now call crystal meth. This miracle drug’s effectiveness against fatigue caught the attention of the director of the Research Institute of Defense Physiology of the German forces, Dr. Otto Ranke, who considered that fatigue was enemy number one of a soldier during battle. An order of thirty five million Pervitin tablets were purchased for the Wehrmacht’s invasion of France in May 1940 to increase effectivity of the campaign that relied especially on speed for success. History will claim that the use of mobile warfare over positional warfare with Germany’s motorized army, French high command mistakes, and an equipment disadvantage led to the ultimate defeat. My research aims to show that Pervitin was a crucial factor in the iron force of the blitzkrieg by the Wehrmacht and Luftwaffe, and that it did not as much come from its tactics, inferiority of the Allies, and employment of mobile warfare but from an army that was blitzed on Pervitin to turn it into a steamroller of a machine that could not be stopped, day or night.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".