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Host-Region: Safe Folklore and the Negotiation of Difference In Post-Socialist Diasporas in Newfoundland

2022· article· en· W4206610514 on OpenAlexafffundvenueabout
Mariya Lesiv

Bibliographic record

VenueAnthropologica · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFolkloreDiasporaEthnic groupGeopoliticsEthnographyContext (archaeology)NegotiationPopulationSociologyImmigrationEthnologyPerspective (graphical)HomelandGeographyGender studiesAnthropologyPolitical scienceSocial scienceArchaeologyDemography

Abstract

fetched live from OpenAlex

Many studies of diasporas focus on (large) locales where sizable diasporic populations provide room for group formation based on a single ethnicity. Scholars often treat such regions as representative of larger units, defining hostland in broad geopolitical categories of countries and even continents. Based on ethnographic research devoted to immigrants from postSocialist Europe and Asia to the Canadian island of Newfoundland, I propose the concept of host-region to emphasize a regional perspective in diaspora studies. The overall small newcomer population and the unique socio-cultural context of the island result in regionally-specific diasporic group-building dynamics, stimulating new Newfoundlanders to expand the notion of their people beyond likeminded co-ethnics. Safe home-region folklore, namely, select cultural expressions that reinforce a sense of unity and do not cause tensions within a group, offers points of connection. However, contrary to many studies that emphasize the notion of commonality within groups, I show that difference, reinforced by continuous turbulence in the home-region, can be equally important in group-building endeavors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.309
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes4
Has abstractyes

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