‘SAGA’: a method to support the practice of critical action learning
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
Critical Action Learning (CAL) is undertaken with an awareness of the persistent tension in organisations between the desire to learn and defences against learning. Attempts to learn in organisations are inevitably bound up with the specific emotional and political context that organisations create, as well as the impact that this has on the outcomes of learning. A key question that arises for CAL practitioners therefore is: what methods or approaches can be used to engage directly with underlying emotions and established power relations? In this paper, one answer to this question is provided. The ‘SAGA’ (Situation, Assumptions, Gut feelings /Emotion, Actions) method is explained and discussed. This model has been designed to engage with emotions and power relations as an integral aspect of action learning. Four examples of SAGA in practice are presented. It is argued that the method offers action learning practitioners an effective approach to CAL, as well as supporting the ongoing process of rethinking and developing it.
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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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