"Kabootar": Towards Informal, Trustworthy, and Community-Based FinTech for Marginalized Immigrants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Financial technology (FinTech) platforms often exclude certain countries from their services due to global political conflicts. As a result, immigrants from these neglected countries struggle with transferring money to and from their homeland through formal mechanisms. Instead, they get involved in informal transnational transactions that, while flexible, are often risky and full of hassles. We looked into this issue through an online survey (n=127) and engaged with multiple stakeholders (n=16), including the Iranian immigrant community in Canada, to co-design an application called ?Kabootar' that matches senders and receivers of money across borders. In this application, a sender-receiver pair is matched with a pertinent pair sending money in the opposite direction. By facilitating two intra-national transactions in local currencies instead of two relatively complicated inter-national transactions, the need for money to cross borders is eliminated while staying within the boundaries of the law. Our user study (n=13) revealed several tensions in users trusting such informal transnational transactions. This work contributes to CSCW, HCI, and social computing's growing scholarship in personalized and collaborative computing technologies by advocating for a novel design approach based on collaboration and informality and extends their scope to the domain of FinTech for politically marginalized communities.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it