Bibliographic record
Abstract
Can Crowds serve as useful allies in policy design? How do non-expert Crowds perform relative to experts in the assessment of policy measures? Does the geographic location of non-expert Crowds, with relevance to the policy context, alter the performance of non-experts Crowds in the assessment of policy measures? In this work, we investigate these questions by undertaking experiments designed to replicate expert policy assessments with non-expert Crowds recruited from Virtual Labor Markets. We use a set of ninety six climate change adaptation policy measures previously evaluated by experts in the Netherlands as our control condition to conduct experiments using two discrete sets of non-expert Crowds recruited from Virtual Labor Markets. We vary the composition of our non-expert Crowds along two conditions: participants recruited from a geographical location directly relevant to the policy context and participants recruited at-large. We discuss our research methods in detail and provide the findings of our experiments.Prpić, J., Taeihagh, A., & Melton, J. (2014). Experiments on Crowdsourcing Policy Assessment. Oxford Internet Institute, University of Oxford - IPP 2014 - Crowdsourcing for Politics and Policy.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".