Introduction to Special Section on Using Microanalysis for Training and Supervision
Bibliographic record
Abstract
This special section focuses on applying microanalysis to video recordings of actual practice as a component of training and supervision in psychotherapy or coaching. We present two articles in which authors report their experiences and a “classic” microanalysis research article (Jordan et al., 2013) that has underpinned developments in training. All three articles use the structured, theoretically grounded microanalytic lens. This lens stipulates a disciplined, utterance-by-utterance approach for observing video recorded practice: (1) focus on specific moments in the interaction, (2) notice what is observable in those moments, and (3) interpret utterances in their sequential context. While disciplined, the microanalytic lens is only descriptive; thus, for training and supervision, one requires a means for incorporating the aims of the therapeutic approach. Viewing dialogue through the microanalytic lens offers insight about what interlocutors are achieving together; incorporating professional aims offers a means for reflecting on practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.029 |
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".