Enacting change through action learning: mobilizing and managing power and emotion
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
This paper reports on a study of how action learning facilitates the movement of knowledge between social contexts. The study involved a community organization that provides educational services related to aphasia and members of a complex continuing care (CCC) practice that received training from the agency. People with aphasia (PWA) (a disability often caused by stroke) retain inherent cognitive competence but have difficulty communicating (speaking, writing, and understanding). The agency has developed a communication technique that improves the ability of PWA to communicate. This project used action learning to introduce a reflective learning cycle into two groups: the agency project team responsible for providing the training and the CCC practice members who received the training. Research participants at both the agency and the CCC facility focused on issues of skill and capacity, and both groups credit the action learning process with introducing a helpful problem-solving cycle into the workplace. CCC participants found that the action learning set provided an emotional container for the anxieties experienced in their workplace. Agency participants found that they were able to use power differences as a way of bringing about beneficial changes.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it