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Record W2937668981 · doi:10.1075/lcs.00003.vig

Language and (in)hospitality

2019· article· en· W2937668981 on OpenAlexaff
Cécile B. Vigouroux

Bibliographic record

VenueLanguage Culture and Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHospitalityIdeologyEthnographyState (computer science)SociologyEpistemologyPolitical scienceAnthropologyPoliticsLawTourismComputer science

Abstract

fetched live from OpenAlex

Abstract Based on a long-term ethnography of Sub-Saharan African migrants in Cape Town, South Africa, this article examines how language as ideology and practice shapes the rules of guesting and hosting and helps (re)configure the on-going positionalities of both the nation-state-defined-host and the foreigner-guest, making murky the distinction between the two. The key notion ofhospitalitydeveloped here is examined aspracticesrather than asidentities.I argue that this theoretical shift makes it possible to unsettle the host and guest positions by not positing them a priori or conceptualizing them as immutable. It likewise makes it possible to deconstruct the categories imposed by the State and by which scholars and policy makers alike abide, such as the dichotomy betweenmigrantsandlocals. At a broader level, the paper draws attention to the Occidentalism that has plagued academia, particularly in the work done on migration. I show how the South African case challenges many scholarly assumptions on language and migration overwhelmingly based on the examination of South-to-North migrations, which do not adequately represent worldwide migrations.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.010
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.275
Teacher spread0.270 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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