Adaptive governance: learning from what organizations do and managing the role they play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to deepen the understanding of adaptive governance, which is advocated for as a manner to deal with dramatic changes in society and/or environment. To re-think the possible contributions of organizations and organization theory, to adaptive governance. Design/methodology/approach Based on social systems theory this study makes a distinction between “governance organizations” and “governance communities.” Organizations are conceptualized as the decision machines which organize and (co-)steer governance. Communities are seen as the social environments against which the governance system orients its operations. This study considers the adaptive mechanisms of organizations and reflect on the roles of organizations to enhance adaptive governance in communities and societies. Findings Diverse types of organizations can link or couple in different ways to communities in their social environment. Such links can enhance the coordinative capacity of the governance system and can also spur innovation to enable adaptation. Yet, linking with communities can also slow down responses to change and complexify the processes of deliberation in governance. Not all adaptive mechanisms available to organizations can be used in communicating with communities or can be institutionalized, but the continuous innovation in the field of organizations can inspire continuous testing of small-scale adaptive mechanisms at higher levels. Society can thus enhance its adaptive capacity by managing the role of organizations. Originality/value The harnessing of insights in organization theory and systems theory for improving understanding of adaptive governance. The finding that both experiment and coordination at societal level are needed, toward adaptive governance, and that organizations can contribute to both.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it