Crowdsourcing in Public Policy: Technologies, Subjects and Its Socio-Political Role
Bibliographic record
Abstract
The article describes the potential of the new interactive crowdsourcing technologies in the public policy. Theconceptual basis of the research implies the network theory of policy, the theory of deliberate democracy and theconcept “governance”. The empirical study was based on the methodology of double reflexivity and conductedusing such tools as case study, focus-group interview and monitoring of the online platforms of Russian andforeign crowdsourcing resources. The authors emphasize and characterize the main technologies of politicalcrowdsourcing: prediction market crowdsourcing, network brainstorming, project crowdsourcing, andcrowdfunding. The authors think that the socio-political role of crowdsourcing lies in the extension of space inwhich the authorities and citizens interact, the initiation and introduction of political innovations, and thedevelopment of the new forms of civil and political participation.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| 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".