Comparing crowdsourcing initiatives: Toward a typology development
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Although numerous studies have examined the crowdsourcing phenomenon, little consensus exists regarding the classification of distinct types of activities within crowdsourcing. In this study, we identify and classify 12 crowdsourcing initiatives that comprise the key categories of crowdsourcing: Crowdpedia , Fansourcing , Crowdnetworking , Crowdsharing , Crowdvoting , Crowdfunding , Ideation , Open Innovation , User Innovation , Scisourcing , Crowd‐Relief , and Open Source Software . Our main objective is to establish the similarities and differences between basic crowdsourcing initiatives and develop a typology based on nine crowdsourcing dimensions that we develop. This crowdsourcing typology will provide a roadmap on which researchers can anchor their research and practitioners can make more informed decisions about which category of crowdsourcing they should seek. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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 it