Bibliographic record
Abstract
Firms and research organizations require humans to annotate raw data to make it compatible with machine learning algorithms. These tasks are often outsourced to individuals worldwide through labor platforms or infrastructures that serve as marketplaces where labour is exchanged as a commodity. The firms that operate them consider workers as “independent contractors” without the social and economic benefits of traditional employment relations. This presentation explores the personal networks of Latin American data workers who train and verify data for machine learning algorithms from their homes. A series of in-depth interviews and an analysis of a self-completion questionnaire and web traffic data suggests that these workers are embedded of networks of trusts build on online and offline interactions. These findings show a continuation of exploitative supply chains in the current artificial intelligence market, where wealthy companies and research institutions in advanced economies profit from the economic and political situation of developing countries to access disembedded labor. This paper concludes by arguing that, though outsourced online labour, artificial intelligence developers not only extract value from their workers, but also indirectly from their communities and personal networks.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".