Expertise Diversity in Online Crowdsourcing Contests: Impacts of Creative Stars on Team Performance
Bibliographic record
Abstract
Online contests, organized by idea-seeking firms through crowdsourcing platforms, harness the collective intelligence of self-organizing teams. Drawing upon research on status hierarchies and motivated information processing in group theory, this study examines the impact of expertise diversity within crowdsourcing teams on team performance, addressing a critical gap in existing literature. After analysis of 5,323 crowdsourcing teams, involving 10,660 contestants on Kaggle.com, the study argues that the effect of expertise variety on crowdsourcing team performance depends on expertise disparity and the relational capital of the ‘creative star’, characterized by centrality and external connectivity. Specifically, it is suggested that the influence of expertise variety on crowdsourcing team performance is positive when expertise disparity is high but negative when expertise disparity is low. Furthermore, this positive (negative) effect under high (low) expertise disparity weakens when the creative star holds a more central position within the crowdsourcing team. Conversely, the positive (negative) effect under high (low) expertise disparity strengthens when the creative star has more external connections during the crowdsourcing contest. This research extends the current understanding of the dynamics of online crowdsourcing contests and the pivotal role of team diversity in achieving team success.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".