Linguistic Isolation: Ferdinand de Saussure’s Linguistic Theory and the Implications for Historiography
Bibliographic record
Abstract
“Linguistic Isolation” concerns the confluence of historical description and language. This essay explores the influence of Ferdinand de Saussure on facticity and description in historiography, arguing that de Saussure’s linguistic theory of significant, signifie, and difference pose problems for any historical account which attempts to describe the past as it actually occurred. Specifically, if we grant de Saussure’s linguistic theory for historical narratives, we are forced to abandon meta-historical entities and concepts, to impose non-empirical interpretive categories on data-sets, limit historical evidences to extremely small data sets, and, perhaps, to abandon the discipline of history altogether. Finally, the essay suggests that if historical descriptions are to be factual and truth-bearing, then the linguistic theory of de Saussure and his contemporary advocates must be contested by every thoughtful historian.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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 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".