The face of internet recruitment: Evaluating the labor markets of online crowdsourcing platforms in China
Bibliographic record
Abstract
Zhubajie/Witmart and other online crowdsourcing platforms have proliferated in China, and researchers have increasingly used them for subject recruitment. One critical question remains, however: what is the generalizability of the findings based on these online samples? In this study, we benchmark the demography of an online sample from Zhubajie to nationally representative samples and replicate commonly asked attitudinal questions in national surveys. We find that online respondents differ from the general population in many respects. Yet, the differences become smaller when comparison is made with the internet users in benchmark surveys. Importantly, when predicting attitudes, our online sample with post-stratification weights is able to produce similar coefficients in most cases as these internet-active subsamples. Our study suggests that online crowdsourcing platforms can be a useful tool for subject recruitment, especially when researchers are interested in making inferences about Chinese netizens. We further analyze the political and social desirability issues of online subjects. Finally, we discuss caveats of using crowdsourcing samples in China.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".