Addressing the challenges of youth migration on entrepreneurial intention: a conceptual focus on Nigeria
Bibliographic record
Abstract
Purpose This study aims to address Nigerian youth migration challenges to their desires to achieve entrepreneurial success in other countries rather than in Nigeria. Design/methodology/approach This study adopts a conceptual approach, relying on the extant literature to explore the topic. Systems and the Brain-drain theories were adopted to explore the connectivity, structure and broad functioning of identified elements toward attain the set purpose of this study. Findings This study identified irregular migration as the most common among Nigerian youths. This study developed a suggested model, identifying factors to address the challenges of youth migration in Nigeria and emancipate their entrepreneurial intentions. These included awareness campaigns, financial support, legal system support, institutional and stakeholder involvement and the embrace of flexibility. Originality/value This study concluded that for an effective synchronization of youth migration and entrepreneurial intentions, the relevant stakeholders identified in this study must play their parts, thereby creating a formidable system that will aid Nigerian youths in achieving entrepreneurial goals without necessarily migrating.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".