Legal status of workers under the sharing economy: a proposal of hybrid employment category
Bibliographic record
Abstract
With the era of internet and mobile phone application, business activities and consumer involvement in the society has drastically changed in many areas, this revolution combined technology with various field of science together to produce a better standard of living known as “Disruptive Technology”. It disrupts the existing industry structures by facilitating commerce using technology-enabled, peer-to-peer and business-to-peer platforms referred to as the “Sharing Economy”. The emergence of work type also creates more complex employment relationships which need a distinction between hire of work and hire of service. As one of the outstanding example of Sharing Economy’s Business, a Transportation Network Company (TNC) which is a ridesharing business such as Uber, Grab and Lyft. It is crucial that an entrepreneur needs to appropriately justify the status of workers in their business since TNC’s driver resemblances both employee and independence contractor. Hence, the author will study theory and regulation from Italy, Spain, and Canada to analyze these countries regulation and experiences of a Hybrid Employment Category, known as “Dependent Contractor”. This will reduce legal uncertainty for a disruptive business not only for TNC but can apply commonly, to protect both TNC and its drivers simultaneously.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".