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Record W2626832149

From Somewhere Else: Transnational Communities and Media

2015· article· en· W2626832149 on OpenAlexaboutno aff
Vandana Pednekar-Magal, Keith Oppenheim

Bibliographic record

VenueScholarWorks - GVSU (Grand Valley State University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsMedia studiesSocial mediaPolitical scienceSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Title: From Somewhere Else: Transnational Communities and Media By: Vandana Pednekar-Magal & Keith Oppenheim The documentary takes a closer look at the use of media and communication technologies among transnational communities in a city Grand Rapids. Transnational/diasporic communities use communication technologies for a plethora of cultural, economic and political activities and create networked spaces that connect the local with the global. These activities constitute the micro-processes of globalization and create transnational spaces in which so-called national identities are reconstituted or hybridized. Networked spaces alter transnational life in the city and temper the defined or imagined unified local culture. The film is based on: 1. In-depth interviews with individuals, families in the following communities: Bosnian, Chinese, Indian, Latin American, and Vietnamese (we have to still complete interview with an African family). 2. Footage of their diasporic (AKA ethnic media), social spaces that have a transnational character places of worship, grocery stores, cultural festival. 3. Expert interviews: Arjun Appadurai (Professor of Media, Culture Society NYU, anthropologist and author of several books on Globalization; Karim.H. Karim (Professor of Media and Culture, Carlton University, Canada, author of several articles books on diasporic media; Martha Gonsalves, Director, Grand Rapids Hispanic Center; Jerry Johnson Director, Community Research Institute, GVSU).

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.255
Teacher spread0.211 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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