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Mapping homelands through virtual spaces: transnational embodiment and Iranian diaspora bloggers

2010· article· en· W2001012025 on OpenAlexaboutno aff
Donya Alinejad

Bibliographic record

VenueGlobal Networks · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaEmbodied cognitionThe InternetSociologyCyberspaceMedia studiesGender studiesWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Abstract In this article I examine Iranian diaspora blogs in an attempt to understand how Iranian bloggers outside Iran create and occupy online transnational spaces. Although it is acknowledged that the internet does not make offline borders and bodies redundant, there is a need to understand how the awareness of bodily presence in offline locations and situations continually informs and shapes online expressions. Through content analysis of English language blogs by Iranians based in the USA and Canada, as well as interviews with diaspora Iranians who read and write these blogs, I advance a concept of ‘transnational embodiment’. The importance of physical travel to, proximity to, and sensory impressions of particular places within two bounded, politically distinct nation‐states shows that diasporas rely heavily on embodied experience in constructing transnational spaces and not only on psychic ties and recalled memories. Members of the second‐generation Iranian diaspora reveal unique types of embodied ties to a diaspora ‘home’ through their apparent search for authenticity.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0050.003
Open science0.0000.004
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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

Citations48
Published2010
Admission routes1
Has abstractyes

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