POLICY, LEGAL AND REGULATION RESEARCH IN THE SHARING ECONOMY: A BIBLIOMETRIC ANALYSIS AND SYSTEMATIC LITERATURE REVIEW
Bibliographic record
Abstract
The sharing economy has changed the way we think about services, assets, and ownership. This phenomenon has shaped a new economic model which emphasises sharing over property ownership. Shared platforms such as Airbnb, Grab, and Uber are increasing in size and popularity exponentially, causing certain political and legal issues associated with such growth. In this regard, this paper aims to investigate the evolution of policy, legal and regulatory research in the sharing economy from the year 1995 to the year 2020 and focuses on new research topics in this field. To achieve this goal, the study utilised extensive bibliometric analysis to identify and analyse 343 articles published in SCOPUS indexed journals from 2004 to 2020. The result shows that research on the sharing economy has increased since 2000. However, the total number of publications in SCOPUS journal relating to policies and regulations still lags behind as compared to the publications in other disciplines. Most of the published research is in the form of concept papers and empirical research. Nevertheless, it is still inadequate. This study summarises the evolution of publications over time and outlines the interests of current research and the potential directions for future research, including addressing policy and organisational research issues in the sharing economy
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.220 | 0.240 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".