Pandemic remittances: transnational exchanges before and during COVID-19
Bibliographic record
Abstract
This study looks at the inflow and outflow of financial and social remittances in Newfoundland and Labrador, and explores how this network of exchanges was impacted by COVID-19. For this research, I undertook semi-structured interviews with 30 im/migrants that had moved to Newfoundland and Labrador, Canada, prior to the onset of COVID-19; I discussed with interviewees their remittance sending and receiving behaviours both before and during the pandemic. This study reveals that, faced with a crisis, transnational individuals living in Newfoundland and Labrador reconfigured their remittance exchanges in order to fill a humanitarian and development role. They sent more financial remittances, decreased their receipt of financial remittances, and engaged in sensitization messaging to promote positive behaviour change. Im/migrants also used their position to educate people in Newfoundland and Labrador regarding the unfolding crisis in other parts of the world, playing a key role in raising public awareness around international issues and, as such, contributing to global citizenship education in the province. The underlying driver for these transnational individuals as they engaged in this development role was relational, caring for others and recognizing the importance of social connections. These roles mirror, in many ways, the approach of humanitarian actors in crisis situations: providing tangible resources, sharing sensitization messaging and engaging the public. This points to the holistic role that transnational individuals and their remittance exchanges play in the global development and humanitarian enterprise. Moreover, applying a Capabilities Approach (Sen, 1999; Nussbaum, 2011) to our understanding of development, we can see that transnational individuals are responding on an individual-level to the needs of friends and family as communicated to them directly. In this way, they are tangibly advancing the development goals of the people in their lives. Existing development literature looking at remittances has narrowly defined their role in crises, often limiting this understanding to the inflow of financial resources to the Global South in times of heightened need. This study offers a holistic insight into the important role that transnational individuals play as developmental actors within the global development enterprise, of which the current literature has not given adequate attention.
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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 | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".