Bibliographic record
Abstract
An array of local and national governments around the world have applied crowdsourcing as a participatory method to engage citizens in political processes. Citizens are invited to share their ideas, perspectives and opinions about matters that traditionally were beyond their access and influence.This book is an introduction to crowdsourcing in policy-making. By introducing case studies from several countries, the book demonstrates how crowdsourcing has been used in participatory budgeting in Canada, federal strategies in the United States, and constitution reform in Iceland. By drawing on these cases, the book analyzes the role of crowdsourcing in democracy. Furthermore, the book summarizes the best practices for crowdsourcing and outlines the benefits and challenges of open processes.The book is based on a report for the Committee of the Future in the Parliament of Finland, delivered by the author in the Spring of 2012. The author, Tanja Aitamurto, is a visiting researcher Program at Stanford University. Due to the at the Liberation Technology author’s academic orientation, this book has an academic touch to it, yet it is also meant to serve as a handbook for crowdsourcing in policy-making.The book is structured as follows. In Chapter 2, we’ll get an overview of crowdsourcing in several fields, thus giving context to the rise of participatory culture. This chapter addresses often posed questions about crowdsourcing and related phenomena like microwork and crowdfunding. Chapter 3 introduces an array of cases, in which crowdsourcing has been used in policy-making. Chapter 4 analyses the role of crowdsourcing in democratic processes, crowdsourcing as a part of Open Government practices, and the impact of crowdsourcing on democracy. Chapter 5 outlines the factors for successful crowdsourcing. Chapter 6 discusses the challenges of crowdsourcing. Chapter 7 gives policy recommendations for enhancing transparency, accountability and citizen participation in the Finnish governance. Chapter 8 concludes the book by encouraging actors in society to experiment with new tools for openness, transparency and accountability.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".