Bibliographic record
Abstract
After years of neglect, political theorists in the last few years have started to take an interest in issues of language policy, and to explore the normative issues they raise. In this chapter, we examine why this interest has arisen and provide an overview of the main approaches that have been developed. A series of recent events has made it clear that language policy is central to many of the traditional themes and concepts of political theory, such as democracy, citizenship, nationhood, and the state. The rise of ethnolinguistic conflict in Eastern Europe, the resurgence of language-based secessionist movements in Catalonia, Flanders, and Quebec, the backlash against immigrant multiculturalism, and the difficulties in building a pan-European sense of European Union citizenship—in all of these cases, linguistic diversity complicates attempts to build stable and cohesive forms of political community. In the past, political theorists have often implicitly assumed that this sort of linguistic diversity would disappear, as a natural concomitant of processes of modernization and nation-building. However, it is now widely accepted that linguistic diversity is an enduring fact about modern societies. As a result, political theorists have started to explore the justifications for minority language rights claims, and to consider how different models of language rights relate to broader political theories of justice, freedom, and democracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".