Towards an index of linguistic justice
Bibliographic record
Abstract
As a step towards a systematic comparative evaluation of the fairness of different language policies, a rationale is presented for the design of an index of linguistic justice based on public policy analysis. The approach taken is to define a ‘minimum threshold of linguistic justice’ with respect to government language policy in three domains: law and order, public administration, and essential services. A hypothetical situation of pure equality and freedom in the choice of language used by all members of society in communicating with the state is used as a theoretical benchmark to study the distributive effects of policy alternatives. Departures from this standard incur lower scores. Indicators are chosen to assess effective access to three kinds of language rights: toleration (the lack of state interference in private language choices), accommodation (accessibility of public services in different languages), and compensation (symbolic and practical recognition of languages outside the dominant one). In order to take account of the cost-benefit trade-offs involved in providing language-related goods to language groups of varying sizes, a method is adopted for weighting scores with respect to compensation rights so that lack of recognition for larger groups incurs greater penalties, while factoring in the particular characteristics of each language-related good. A trial set of ten indicators illustrates the compromises entailed in balancing theoretical rigour with empirical feasibility.
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 | 0.057 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.030 | 0.017 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".