Bibliographic record
Abstract
Steering has negative connotations nowadays in many discussions on governance, policy, politics and planning. The associations with the modernist state project linger on. At the same time, a rethinking of what is possible by means of policy and planning, what is possible through governance, which forms of change and which pursuits of common goods still make sense, in an era of cynicism about steering yet also high steering expectations, seems eminently useful. Between laissez faire and blue-print planning are many paths which can be walked. In this thematic issue, we highlight the value of evolutionary understandings of governance and of governance in society, in order to grasp which self-transformations of governance systems are more likely than others and which governance tools and ideas stand a better chance than others in a particular context. We pay particular attention to Evolutionary Governance Theory (EGT) as a perspective on governance which delineates steering options as stemming from a set of co-evolutions in governance. Understanding steering options requires, for EGT, path mapping of unique governance paths, as well as context mapping, the external contexts relevant for the mode of reproduction of the governance system in case. A rethinking of steering in governance, through the lens of EGT, can shed a light on governance for innovation, sustainability transitions, new forms of participation and self-organization. For EGT, co-evolutions and dependencies, not only limit but also shape possibilities of steering, per path and per domain of governance and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".