Collaborative crowdsourcing with crowd4U
Bibliographic record
Abstract
Collaborative crowdsourcing is an emerging paradigm where a set of workers, often with diverse and complementary skills, form groups and work together to complete complex tasks. While crowdsourcing has been used successfully in many applications, collaboration is essential for achieving a high quality outcome for a number of emerging applications such as text translation, citizen journalism and surveillance tasks. However, no crowdsourcing platform today enables the end-to-end deployment of collaborative tasks. We demonstrate Crowd4U, a volunteer-based system that enables the deployment of diverse crowdsourcing tasks with complex data-flows, in a declarative manner. In addition to treating workers and tasks as rich entities, Crowd4U also provides an easy-to-use form-based task UI. Crowd4U implements worker-to-task assignment algorithms that are appropriate for each kind of task. Once workers are assigned to tasks, appropriate worker collaboration schemes are enforced in order to enable effective result coordination.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".