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Record W3086316501 · doi:10.1080/01490400.2020.1817201

The Development of Social and Cultural Capitals for Immigrant Hosts of VFR Travellers

2020· article· en· W3086316501 on OpenAlexaff
Tom Griffin, Troy D. Glover

Bibliographic record

VenueLeisure Sciences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsHabitusSocial capitalSociologyImmigrationContext (archaeology)Social isolationCultural capitalIdentity (music)Social identity theorySense of communityField (mathematics)NarrativePublic relationsGender studiesSocial groupPolitical sciencePsychologySocial scienceAesthetics

Abstract

fetched live from OpenAlex

The hosting of friends and relatives is an important and common experience. Hosting gives reason and opportunity for residents to explore their community while spending time with guests. For newcomers, hosting brings additional significance due to their relative isolation from traditional social networks and comparative unfamiliarity with the community. This narrative inquiry examines immigrants’ experiences of hosting friends and relatives using Bourdieu’s theories of social and cultural capital. Findings suggest that hosting creates a context where relationships are refreshed, resources exchanged, and capitals that enhance hosts’ habitus in the new social field are developed. This impacts personal identity, sense of accomplishments, and increases familiarity with the new culture that may further integration. Application of both social and cultural capital demonstrates the opportunities and dilemmas for immigrant hosts interacting in dual social fields. A theoretical model delineates processes and implications for capital development laying foundations for future research and practitioner guidance.

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)0.002
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.332
Teacher spread0.269 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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