The Emergence of the Sharing Economy: Implications for Development
Bibliographic record
Abstract
With the spread of internet-based technologies, the sharing economy is emerging as a new and rapidly growing sector of the economy. This sector offers transformative potential for many other sectors of the economy, and possibilities for new economic activity and growth in the developing world. The sharing economy is a misnomer, as while there are possibilities for more cooperative economic approaches, the primary emphasis is on the reduction of transaction costs including the elimination of middlemen in sales between a good/service provider and a customer. In this introductory article to the special edition, we provide an overview of both the positive and negative potential for the contribution of the sharing economy to development. On the one hand, we find that the reduction in transactions costs and the low price of mobiles improves access to goods and services, and reduces the need for economies of scale for marginalized groups who lack access to capital and infrastructure. However, we point to the real obstacles that the poor experience in using internet-based platforms to start businesses or accumulate capital. We discuss the potential for labour substitution of traditional service providers, such as taxi drivers. In juxtaposition to some of its claimants, we find that the sharing economy changes the nature of institutional, regulatory and promotional challenges by the state and social groups, rather than reducing the need for them.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".