Commons and Policy: Compilation of Inputs and Reflections – IASC Europe and CIS Colloquium Series 2022
Bibliographic record
Abstract
Between March and May 2022, IASC Europe & CIS in collaboration with the Institute of Social Anthropology hosted a colloquium series featuring presentations by international speakers. Thematically, the presentations within the series were dedicated to policy issues related to the commons and bringing on the one hand, the commons knowledge and on the other hand, policy and practice closer in Europe & CIS and beyond. Seven themes were used to be presented in relation to policies: urban commons, European agrarian commons, implementation of commons policies, health commons, digital commons and the SDGs and the commons. Background for the colloquium was the fact that at the moment many policies related to agrarian subsidies, sustainable development goals, biodiversity protection, energy strategies, land use planning etc. are being discussed in Europe and CIS on national or on EU and other respective supranational levels. Europe and CIS is also closely linked to development policies in other regions through its active role in bilateral and global processes. Some of these policies are already on the way to be implemented by governments. Many of these issues are closely related to the commons but this is often not mentioned. Despite Ostrom's work, the commons are not an issue in these policies although highly relevant, especially when it comes to the issue of common property and commoners’ interests. The presentation addressed these issues and discussions were focused on how this could be changed: Commons and Polycentric Governance within and across cities by Chagas Cavalcanti AR (and Roggero M) The European commons: The “great absent” in the EU agri-food and agri-environmental policy making by Manzoni A, Diaz-Maroto IJ, Bogataj N Water as commons in undemocratic postcolonial South by Putri P Success and failures in policy implementation – Methodological questions and sharing goals with commoners by Micciarelli G (and Mendez P) Health and law-making: re-creating collective narratives by Balli F, Carpentier P Is the digital economy market-driven or commons-based? Lessons for EU digital strategy by Pazaitis A, Kostakis V SDGs and the commons: from a central missing topic towards recognition via national implementations? by Haller T, Soliev I
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".