‘SAGA’: a method to support the practice of critical action learning
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.
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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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".