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Record W4327731754 · doi:10.1177/1470594x231158657

Towards an index of linguistic justice

2023· article· en· W4327731754 on OpenAlex
Michele Gazzola, Bengt‐Arne Wickström, Mark Fettes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolitics Philosophy & Economics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsPublic economicsLanguage policyEconomic JusticeIndex (typography)Distributive justiceComputer scienceLaw and economicsEconomicsSociologyMicroeconomics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.447
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it