Where Does the Data Go? Data Modelling and Reuse in Crowdsourcing for Social Innovation
Bibliographic record
Abstract
Crowdsourcing is an exemplar of how technology can enhance collaboration and problem-solving. Social innovations are solutions to social problems that are more effective, efficient, sustainable, or just than existing solutions. Crowdsourcing for social innovation (CfSI) platforms are proliferating. In this paper, we frame these platforms as data crowdsourcing projects because contributions contain data about social innovation challenges and solutions. However, CfSI platforms are not necessarily designed with the potential of this data in mind. In turn, this data may be poorly modelled, semi-structured, or unstructured, and therefore the true value of contributions may not be fully realized. We propose a design science research project that investigates the data-based challenges and opportunities of CfSI. Our goal is the development of theory that guides the design of CfSI platforms as data crowdsourcing platforms, enabling effective management and reuse of the valuable data these platforms collect.
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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 | 0.005 | 0.002 |
| 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 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".