Action learning for democracy: Introduction to a special issue
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.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".