Action learning for democracy: Introduction to a special issue
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
Democracy as a governing principle seems to be more in question now than at any time since WW2. In countries with democratic systems of government, often hard won over centuries of struggle, the social democracies that we have taken for granted are experiencing crises of legitimacy. Many are beset by widespread disillusionment and the emergence of populist and authoritarian parties which do not subscribe to familiar democratic values. In work organisations, there is usually a striking "democratic deficit” and wide disparities of power and voice. Despite research evidence for the superiority of collaborative and cooperative leadership in uncertain conditions, hierarchical principles are as evident as ever and tend to usurp attempts at democratic decision making. In this issue of the Journal we make a case for action learning as an enabler of democratic processes and as a means reviving faith in democracy as a way of working and living together. We hope that this Special Issue will be an inspiration to everyone working with action learning to encourage democratic practices in organisations and society. Our contributors make arguments and present cases in support of this aim, reporting from a great variety of locations including Greek teacher learning networks, Swedish preschools, a Citizens' Assembly in Germany and an effort to develop democratic competencies with students in war-torn Ukraine.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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