The gig economy in Chile: Examining labor conditions and the nature of gig work in a Global South country
Bibliographic record
Abstract
While there is growing literature regarding the impact of the gig economy in countries of the Global North, the way it operates in Latin America and the Caribbean remains underexplored. This article describes platform work in Chile, especially in the context of COVID-19, which has highlighted the essential role of geographically tethered digital platforms in facilitating essential goods and services in times of social distancing and quarantine. While the gig economy has provided employment for those outside traditional labor markets, its supposedly ‘collaborative’ employment structures obscure the different costs of precarity and informality transferred from platforms to workers (Ravenelle, 2019). Based on 35 interviews with gig workers using the Fairwork framework to evaluate working conditions in the gig economy, this article examines digital labor relations, both on paper and in reality; the conditions and limitations gig workers face daily; and their perceptions regarding such platforms. We discuss the contradictory experiences felt by platform workers, dependent on the platform in some ways, and independent in others. We argue that the inherently contradictory conditions and circumstances of platform work have become even more salient for gig workers in the context of COVID-19: risks increasingly fall on workers as platforms continue to stress their ‘choice’ to do so. This article reveals that the nature of the linkage between platform and worker is eminently a labor relationship, with clearly established elements of worker dependence.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".