‘This war for men’s minds’: the birth of a human science in Cold War America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The past decade has seen an explosion of work on the history of the human sciences during the Cold War. This work, however, does not engage with one of the leading human sciences of the period: linguistics. This article begins to rectify this knowledge gap by investigating the influence of linguistics and its concept of study, language, on American public, political and intellectual life during the postwar and early Cold War years. I show that language emerged in three frameworks in this period: language as tool, language as weapon, and language as knowledge. As America stepped onto the international stage, language and linguistics were at the forefront: the military poured millions of dollars into machine translation, American diplomats were required to master scores of foreign languages, and schoolchildren were exposed to language-learning on a scale never before seen in the United States. Together, I argue, language and linguistics formed a critical part of the rise of American leadership in the new world order - one that provided communities as dispersed as the military, the diplomatic corps, scientists and language teachers with a powerful way of tackling the problems they faced. To date, linguistics has not been integrated into the broader framework of Cold War human sciences. In this article, I aim to bring both language, as concept, and linguistics, as discipline, into this framework. In doing so, I pave the way for future work on the history of linguistics as a human science.
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.
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 | 0.002 | 0.000 |
| 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 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it