Learning from each other for governance: Transatlantic,transdisciplinary knowledge exchange for governance innovation. EDAP 4/2016
Bibliographic record
Abstract
If traditionally citizens’ influence in the design of strategic goals and policies was limited by the right to vote, the last few decades witnessed the emergence of a normative discourse and the implementation of different initiatives, challenging the representative democracy to offer a structure capable of enabling the dialogue between those elected and voters. By proposing a number of participatory alternatives empowering citizens, some of the democracies using those concepts are committed towards a culture of participatory governance. The two authors of this paper argue that learning and experimenting are crucial elements to find new forms and methods for participatory governance in order to strengthen democratic cultures, enhance the resonance capability to react to current societal challenges and to be able to jointly work on creating a sustainable future for today’s and tomorrow’s generations. The aim of this paper is to explore how different innovative experiments with participatory governance in different regional contexts, can learn and benefit from each other in a transdisciplinary setting. To do so, two case studies from Canada and Austria are presented, compared and analyzed: On the one hand, the British Columbia Citizens’ Assembly in Canada as an initiative designed to develop collaborations between governments and citizens regardless of their age, sex, ethnicity, cultural background or social statues and on the other hand the project “URB@Exp: Towards new forms of urban governance and city development: learning from urban experiments with living labs & city labs” in Leoben, Austria. The paper will analyze and compare the strengths, weaknesses, opportunities and threats of these two case studies and put the results of this comparison within a transdisciplinary, transatlantic framework. In this specific setting the practical perspective based on the Canadian case study meets the scientific perspective based on the Austrian case study, whereby a unique opportunity is created to be involved in a mutual scientific-societal learning process and develop new knowledge for innovative governance. The paper will present key findings of this learning process and reflect on how they can be used to generate democratic participation, assist public policy development, improve governance performance and strengthen society-science collaborations in order to initiate sustainable development processes.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".