The Impact of Social Media Monetization on Youth Employmentand Economic Productivity in Nigeria’s Labor Market
Bibliographic record
Abstract
s study explores the impact of social media monetization on youth employment and economic productivity in Nigeria’s labor market. As digital platforms like Instagram, YouTube, and TikTok offer youngNigerians opportunities for income generation, they also reshape traditional employment patterns. Theresearch examines the dual-edged nature of social media monetization, balancing the potential for eco-nomic empowerment against the challenges of job security, income volatility, and productivity. UtilizingMultinomial Logistic Regression, the study analyzes survey data from Nigerian youth engaged in digi-tal entrepreneurship to determine the effects on employment outcomes and economic productivity. Keyfindings highlight that while social media provides flexible employment opportunities, it may also diverttime from other productive activities, with implications for long-term economic growth. The study con-cludes with policy recommendations to optimize the benefits of digital entrepreneurship while addressingits challenges, aiming to integrate these emerging work patterns into Nigeria’s broader economic framework.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".