The Foundations of Naval Science: Alfred Thayer Mahan's The Influence of Sea Power on History and the Library of Congress Classification System
Bibliographic record
Abstract
This article is a history of the creation of the Naval Science class within the Library of Congress Classification System (LCCS) during that system’s fashioning and development at the turn of the twentieth century. Previous work on the history of classification and especially of the LCCS has looked closely at the mechanics of the creation of such systems and at ideological influences on classification schemes. Prior scholarship has neglected the means by which ideologies are encoded into classification systems, however. The present article examines the history of a single class by looking at the ideological and political assumptions behind that class and the means by which these assumptions were written into the LCCS. Specifically, we argue that the Naval Science class resulted from a concerted effort by naval theorists to raise their field to the status of a science, the interest of Washington’s political class in this new science as a justification for imperial expansion, and a publishing boom in naval matters as the American public became eager consumers of such work during the Spanish-American War. This complex narrative thus illustrates the manifold influences on the creation of any classification system and asks us to consider that multiplicity of influences, whether we as librarians teach about existing systems or work to build new ones.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".