Shadow facilitation: creating conditions for facilitator reflection and action in critical action learning
Bibliographic record
Abstract
Critical Action Learning (CAL) involves a deliberate choice to engage with emotions and power relations generated by people’s attempts to learn. This choice recognizes that there are likely to be implicit limits to learning in organizations, especially where such learning starts to undermine established ways of thinking and working. One way to manage the demands of engaging with underlying emotions and power relations is to utilize shadow facilitation as a method for facilitator reflection and action. Shadow facilitation is the process through which CAL facilitators work with an experienced, independent other to reflect on their interventions within an action learning set. The role of the shadow facilitator is to listen for the emotions and power relations that emerge from reflection and to offer them back as information to support the facilitation of organizing insight. In this paper we present and illustrate the main ideas that inform shadow facilitation and reflect on the possibilities and limitations of this method for generating organizing insight. We argue that the value of shadow facilitation is that it helps CAL facilitators to work directly on the intense emotions and complex power relations embedded in and evoked by the facilitation role.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".