Bibliographic record
Abstract
In Chapter 2, I argue that our ability to distill and evaluate what “multilingualism” is has been jarred, since the 1970s and even before, by profound transformations in political economy and its systems of value. An idealized economistic version of “multilingualism” has been engineered, for various mono- and multilingual subjects to embody or resist. I describe this production of an economistic model of “ordolingualism” as a mimesis of a mimesis, or what Michael Taussig has called a “mimetic excess” (1993, p. 252). Since the 1990s, this economistic dispositive of “ordolingualism” has functioned, with increasing allure, as an authoritative global infrastructure I call supralingualism, which is designed to suppress, manage, and alleviate the complex subjective and interactional experience of lived multilingualism.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".