News of Difference: Understanding, Highlighting, and Building Exceptions in Solution-Focused Brief Therapy
Bibliographic record
Abstract
Exceptions play an integral role in solution-focused brief therapy's solution-building conversations. The nuanced nature of exceptions can make them challenging to learn and teach. Students and novice clinicians may relinquish the search for exceptions prematurely when it fails to yield the desired results. Teachers and supervisors may experience difficulties in articulating the process of searching for exceptions. This article is an exploration of ways to understand the search for exceptions and how to highlight and build exceptions into possibilities for change. Brief case examples are used to illustrate what to look for in the search for exceptions. In addition, sample questions are included to help clinicians locate, highlight, and build exceptions in the therapeutic dialogue. Finally, limitations and ideas regarding the usefulness of these concepts in practice, teaching, and supervision are discussed.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".