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Communicating Citizenship

2019· article· en· W4236528700 on OpenAlexaff
Alejandro I. Paz

Bibliographic record

VenueAnnual Review of Anthropology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCitizenshipSubalternSociologyIndigenousSociolinguisticsPoliticsState (computer science)NormativePublic sphereGender studiesImmigrationLinguisticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Citizenship has become a major topic in anthropology and the study of language (including sociolinguistics) since the early 1990s, with scholars in these fields especially examining the status and political claims of immigrants, refugees, indigenous groups, and other subaltern populations. This article argues that models of communication lie at the heart of debates about citizenship and explores two fundamentally communicative processes: first, the mutual recognition of citizens as citizens, and second, the interpellation by state apparatuses of citizens. It first discusses the emergence of the question of citizenship within anthropology and the study of language. It then considers the tension that arises as any recognition of difference confronts the normative model of citizenship already institutionalized in the state apparatus. Finally, this article examines the interlacing of these scholarly trajectories in one of the premier sites where citizens communicate as citizens: the public sphere.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.019
Scholarly communication0.0100.011
Open science0.0010.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.065
GPT teacher head0.514
Teacher spread0.448 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2019
Admission routes1
Has abstractyes

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