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Record W3197900767 · doi:10.1111/glob.12342

Mapping the national web: Spaces, cultures and borders of diasporic mobilization in the digital age

2021· article· en· W3197900767 on OpenAlexaff
Olga Boichak, Priya Kumar

Bibliographic record

VenueGlobal Networks · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUkrainianGeopoliticsContext (archaeology)MobilizationNational identityPolitical scienceNational IdentitiesMedia studiesWorld Wide WebSociologyGeographyLinguisticsComputer sciencePolitics

Abstract

fetched live from OpenAlex

Abstract National web is a series of interlinked online spaces, generated and visited by users who share a common national identity, language and/or an interest in a particular country. For diasporic communities living outside of their country of origin, national web is an entity that emerges through the production and circulation of culturally significant content and genres. A wealth of textual and visual data, produced in the process of mediated communication among diasporic actors, turn social media into a point of entry for mapping national webs. In this paper, we explore hyperlinking behaviours among Ukrainian Canadians to map geographic, linguistic and geopolitical boundaries of the Ukrainian national web. Shining light on the spaces and cultures of diasporic mobilization in the digital age, we identify distinct web spheres that mediate the Ukrainian Canadians’ relationship to their country of origin, demonstrating their elevated significance in the current geopolitical context.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0050.004
Scholarly communication0.0050.003
Open science0.0000.004
Research integrity0.0000.000
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 head0.024
GPT teacher head0.306
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 source (direct Gemma or distilled Codex), 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

Citations10
Published2021
Admission routes1
Has abstractyes

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