Bibliographic record
Abstract
Using a historical institutionalist approach, I demonstrate how institutionalized norms stemming from the liberal tradition in America have informed its language regime by tracing the path dependency of language policy and the critical junctures when changing norms lead to policy shifts. In the early republic, liberal norms enshrined in the Constitution informed a minimalist language regime. At the turn of the 19th century, norms shifted to reflect rapid industrialization and mass immigration, informing attempts at restrictive language policies. At the critical juncture of the civil rights movement, the monolingual language regime was challenged by new norms of what constituted a liberal democratic society. Neoliberal norms of the Reagan presidency facilitated the success of the English-only movement in changing language policies at the state-level. Neoliberal cosmopolitanism of the new millennium re-introduced minimal multilingual policy initiatives. I conclude by suggesting that Trump’s election represents a shift to nationalist, albeit possibly illiberal, norms.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".