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Record W2616030021 · doi:10.1080/00083968.2016.1277149

“A river is not a boundary”: interplays of national and linguistic citizenship in Pulaar language activism

2017· article· en· W2616030021 on OpenAlexvenueno aff
John Hames

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersWest African Research Association, Boston University
KeywordsCitizenshipSociologyPopulationPoliticsResistance (ecology)State (computer science)Political scienceLiteracyColonialismLinguisticsGender studiesMedia studiesLaw

Abstract

fetched live from OpenAlex

This article examines the interplay of linguistic citizenship and national citizenship within a trans-border language movement. Since the late 1950s, language activists from among the Haalpulaar’en of Senegal and Mauritania have practiced forms of literacy teaching, literary production, theater and journalism in promoting their language, known as Pulaar. These activists’ trans-border collaborations and their emergence from two distinct national contexts – where, in both cases, Pulaar is spoken by a minority of the population – must be understood in relation to one another. Tracing the biographical itineraries of several key activists, this article illustrates how Senegalese and Mauritanian Pulaar militants have collaborated when it comes to language promotion yet frame their grievances within their respective national political arenas. More than a form of local resistance based on trans-border linguistic and cultural ties, Pulaar language activism has emerged thanks to opportunities presented by forms of post-colonial state-building, including the creation of national radio.

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)0.005
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.020
Scholarly communication0.0090.006
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.091
GPT teacher head0.384
Teacher spread0.294 · 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
GenreEmpirical

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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of African Studies / Revue canadienne des études africainesSame topicMultilingual Education and PolicyFrench-language works237,207