How can working conditions for online crowdworkers be improved? Institutional experiments for cross-jurisdictional polycentric work
Bibliographic record
Abstract
This article articulates the theory and design of the Crowdsourcing Wage Pledge, an action research initiative exploring how academic users of paid crowdsourcing can contribute to improving the working conditions of online crowdworkers. ‘Crowdwork’ describes remote information work comprising short tasks – for example, surveys, transcription, translation, data cleaning – organized through online platforms (for example, Amazon Mechanical Turk, Clickworker, Scale API) and typically remunerated under a self-employed piecework model. The digital labour platforms mediating this work are important in a growing range of sectors, having become, for example, key nodes in the global supply chains for artificial intelligence products, including self-driving cars and chatbots (for example, ChatGPT). Academics also use crowdsourcing – social scientists, for example, to recruit study participants, and computer scientists to outsource data-cleaning work. While crowdwork creates new income opportunities with, sometimes, greater time flexibility and lower barriers to entry than traditional employment, crowdworkers typically earn less than traditional employees doing similar work, often below minimum wage, and face other decent work deficits, including arbitrary nonpayment and termination and misclassification. While some jurisdictions are developing laws to address these challenges, these typically focus on in-person platform work (for example, delivery). Crowdwork, however, is often cross-jurisdictional and therefore presents challenges for national regulation. The Crowdsourcing Wage Pledge is a voluntary regulatory initiative aiming to recruit academic users of crowdsourcing into adopting best practices identified by previous research. It is informed both by past practical efforts to improve crowdwork working conditions and by theory from political science, human-computer interaction, and industrial relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".