Bibliographic record
Abstract
Due to the interest of organizations and academics, crowdsourcing is emerging as an area of targeted social networking. The recent popularity and notable rise of crowdsourcing provides us with the opportunity to study these emerging communities to standardize and facilitate the crowdsourcing process for future development of such platforms. In this paper, we conduct a large and comprehensive study of the structure of a number of crowdsourcing communities (CCs). We study various properties of association (ASSO) and interaction (INTR) networks in an attempt to compare them with existing networks, such as online social networks (OSNs) and the World Wide Web (WWW) network. We obtained data for five successful CCs with nearly two million vertices and nearly six million edges, as well as data for four popular social network sites, Flickr, YouTube, Orkut, and LiveJournal, with more than 11 million vertices and over 328 million edges. We also obtained WWW data containing over 18 million vertices and over 64 million edges. We believe this is the first structural comparative study of CC networks with social and WWW networks at this scale. Our study reveals that CC networks-both ASSO and INTR- are smaller and less symmetrical than OSNs. Similar to OSNs and WWW, degree distributions of CC networks follow powerlaw distribution. CCs and WWW do not suffer influence dilution as is the case in OSNs. Different than OSNs, members of CC networks tend to connect to others with varying degrees, as is the case with WWW.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".