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Record W4360948890 · doi:10.1386/hosp_00060_1

Multicultural hospitality and immigration in Winnipeg, Manitoba: Host–guest dynamics

2023· article· en· W4360948890 on OpenAlexaffabout
Nathalie Piquemal, Faïçal Zellama, Leyla Sall, Bathélemy Bolivar

Bibliographic record

VenueHospitality & Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversité de Saint-BonifaceUniversité de MonctonUniversity of Manitoba
Fundersnot available
KeywordsHospitalityMulticulturalismSociologyImmigrationPower (physics)State (computer science)Gender studiesPolitical scienceTourismPedagogyLaw

Abstract

fetched live from OpenAlex

Relying on qualitative data obtained from newcomers in Winnipeg, Manitoba, this article critically examines hospitality, specifically host–guest dynamics, with special attention to cultural discontinuities and contentious policies on foreign credentials. In particular, this article sheds light on the contested nature of hospitality practices, thereby moving beyond the notion of vertical power relations between the nation state as host and immigrants as guests to acknowledge the existence of everyday reciprocal practices of welcoming and supporting one another, such as those occurring in ethnocultural communities. In order to highlight the challenges related to the implementation of the principles of hospitality, we begin by making a brief presentation of the dimensions of this concept and by problematizing multicultural hospitality with special attention to critical multiculturalism. Based on qualitative data, we then problematize hospitality with special attention to social relations and cultural discontinuities. Finally, we conclude with a discussion on guest and host factors in the concretization of multicultural hospitality with and for immigrants.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.258
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.006
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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