Bibliographic record
Abstract
One-way lectures, status reports, brainstorming, and open and managed discussions can all be tedious, alienating and demoralizing exercises of unbalanced power. We see opportunities to rethink in-person and online interactions across spheres such as workplaces, classrooms, conferences, and movement organizing. We share essential principles of Liberating Structures (LS), a set of 33+ open-source methods for more engaging and effective gatherings. We offer visual illustrations, practical examples, and insights from our experiences using LS for teaching and facilitation. LS, named by action researcher William Torbert and elaborated by Henri Lipmanowicz, Keith McCandless and others, are grounded in complexity thinking (vs. linear machine models), observing that innovation emerges from interconnectedness and non-linear feedback. LS thus attend to “micro-structures” of convenings to better organize participants’ time and attention: the invitation, participant distribution, timing and steps, group configurations and space arrangements. Facilitators can adopt, adapt, repeat and combine methods like Open Space, Troika Consulting, Drawing Together, and Impromptu Networking to support gatherings of any size. We believe LS can ease the work of dismantling oppression and reassembling the new pluriversal worlds we seek, by supporting communities of learning, design and social change in organizing inclusive gatherings, challenging institutional norms, and building alternative visions.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".