Leveraging Diaspora Contributions for Economic Resilience in Nigeria During a Recession
Bibliographic record
Abstract
This study examines the pivotal role of the Nigerian diaspora in bolstering economic resilience during periods of recession, focusing on the critical issue of economic instability in Nigeria. Diaspora remittances have become a cornerstone of Nigeria’s socio-economic development, providing vital support for families, small businesses, and infrastructure projects. Utilizing the RBV, the research adopts a qualitative approach, drawing insights from 10 Nigerian diaspora members residing in the United States, Canada, the United Kingdom, Germany, and France. Participants were purposively selected for their active engagement in remittances, investments, and developmental initiatives. The findings reveal three key types of contributions. Financial remittances stabilize households, fund small businesses, and drive infrastructure development. Social contributions, facilitated by robust diaspora networks, foster global partnerships, mobilize funding, and advocate for improved governance. Intellectual contributions, including knowledge transfer and skill-building, enhance capacity in critical sectors such as healthcare, education, and technology, fostering innovation and sustainable growth. Despite these significant contributions, the study identifies barriers to effective diaspora engagement. These include restrictive government policies, high transaction costs, economic instability, and a lack of trust in institutional frameworks. Participants propose actionable strategies to address these challenges, such as implementing tax incentives, reducing remittance fees, streamlining bureaucratic processes, establishing mentorship programs, and fostering stronger collaboration between diaspora groups and government entities. This study underscores the transformative potential of the Nigerian diaspora in driving economic recovery, resilience, and sustainable development. By integrating diaspora contributions into national development frameworks and addressing systemic barriers, policymakers can unlock the full potential of this critical resource. The findings provide a foundation for further research and offer practical recommendations to optimize diaspora engagement in times of economic adversity.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".