From Somewhere Else: Transnational Communities and Media
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".