Bibliographic record
Abstract
This chapter offers a normative engagement with language policy and politics, particularly involving the moral evaluation of power structures associated with language, and their possible alternatives. Questions about language rights and linguistic equality, the compatibility between particular language regimes and democratic principles, and the global ethics of English as a lingua franca, as well as emerging debates in political philosophy on linguistic justice, involve language ethics, namely, inquiry on the moral problems, practices, and policies related to language. Language ethics must be fundamentally interdisciplinary, not merely to bridge political philosophy and applied linguistics, but rather to combine their distinct scientific epistemologies in a principled and systematic way. The concluding section of the chapter turns its attention to the intrinsic tension between the aim of language policy to achieve particular moral outcomes and the messy, uncertain, and often unpredictable realities that shape local and global social change.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".