Digitization and the Contract Labor Market: A Research Agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online contract labor globalizes traditionally local labor markets, with platforms that enable employers, most of whom are in high-income countries, to more easily outsource tasks to contractors, primarily located in low-income countries. This market is growing rapidly; we provide descriptive statistics from one of the leading platforms where the number of hours worked increased 55% from 2011 to 2012, with the 2012 total wage bill just over $360 million. We outline three lines of inquiry in this market setting that are central to the broader digitization research agenda: 1) How will the digitization of this market influence the distribution of economic activity (geographic distribution of work, income distribution, distribution of work across firm boundaries)?; 2) What is the magnitude and nature of information frictions in these digital market settings as reflected by user responses to market design features (allocation of visibility, investments in human capital acquisition, machine-aided recommendations)?; 3) How will the digitization of this market affect social welfare (increased efficiency in matching, production?)? We draw upon economic theory as well as evidence from empirical research on online contract labor markets and other related settings to motivate and contextualize this research agenda.
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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 | 0.033 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it