Facebook and WhatsApp as Elements in Transnational Care Chains for the Trinidadian Diaspora
Bibliographic record
Abstract
Despite being separated by great geographical distances, the Trinidadian Diaspora community has managed to stay in regular communication with those back “home” using the latest available technologies. Trinidadian migrants living abroad have established multi-directional care chains with family, kin, and friends that have endured for decades. This social connection has evolved from letter writing, telegrams, telephones, emails, and most recently, internet-based social media which includes: Facebook, WhatsApp, Skype, Facetime, Snapchat, Twitter, and Google Hangout. This paper examines how social media, focusing on Facebook and WhatsApp, are tools being used by the Trinidadian Diaspora to provide transnational care-giving to family and friends kin left behind in the “home” country and beyond. The analysis is based on the results of two online Qualtrics surveys, one implemented in 2012 (n = 150) and another in 2015 (n = 100) of Trinidadian Diaspora participants and in-depth interviews with (n = 10) Canadian-Trinidadians. This paper explores how social media have become a virtual transnational bridge that connects the Trinidadian Diaspora across long distances and provides family members with a feeling of psychological well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".