Bibliographic record
Abstract
The purpose of this study was to examine the meaning and value of 'making magic,' facilitating collaborative processes. In this study the term 'making magic' is a metaphor for the peak experiences that happen when facilitating collaborative processes with groups (two or more people) in workplace, community, and/or classroom settings. Four other educational/organizational consultants joined me in this inquiry that was itself a collaborative process that I facilitated in three stages: interviews; collaborative conversation; and data analysis. Two key areas of literature, appreciative inquiry and transformative education, inform and are informed by this study. I used appreciative inquiry as a research methodology and the models, theories and applications of appreciative inquiry inform our practices of 'making magic' The transformative education literature added a critical lens that is lacking in appreciative inquiry, the notions of the impact of social structural differences on people's ability to appreciate and be appreciated. The primary findings of this inquiry are the notions of critical appreciative processes, and 'making magic' through being present, vulnerable and courageous. Critical appreciative processes combine the appreciative and the critical. These processes could enhance the possibility of magic, the transformation that happens when groups of people collaborate effectively by being interconnected and authentic, present, with each other. The group transforms to be more than the individuals put together and/or the group process aggrandizes the learning. Critical appreciative processes could create sacred spaces, holistic spaces that take into consideration the spirit and emotions as well as the intellect and body. Facilitators intentionally create these spaces for the possibility of magic through a variety of strategies and by being present, vulnerable and courageous themselves, being who they are, as they facilitate. The collaborative group in this inquiry moved beyond the initial peak experiences of their work to critically examine the challenges of 'making magic' It is hard work. But the value of the work is the possibility of constructive change in the worlds within which we facilitate. It is of value to us as well, because we continue to learn and grow through our 'magic making' work.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".