Humans as a Service: The Promise and Perils of Work in the Gig Economy, by Jeremias Prassl
Bibliographic record
Abstract
The gig economy is “the collection of markets that match providers to consumers on a gig basis in support of on-demand commerce.” Some of the most recognizable examples in Canada are Lyft and Uber. In a September 2017 report by the Bank of Montreal on the gig economy, it was estimated that 2.18 million Canadians were categorized as temporary workers, which include people who take on term, contract, or temporary employment, such as freelancers. This report defines a gig as “any job, especially one of short or uncertain duration.” A recent Ontario Court of Appeal Case, Heller v Uber Technologies Inc, identifies the important step needed to determine whether a worker is an employee or not. In the United States, there has been lawsuit after lawsuit which leads to settlement after settlement in the gig economy. Most of the conflicts centre around how workers are classified and benefit (or fail to benefit) as a result.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".