If the Measure Doesn't Fit, Invent One that Does: Developing Individualized Feedback Measures for Supervision
Bibliographic record
Abstract
Acting to counter the constraining effect of power practices on supervisee openness and risk-taking is particularly important for supervisors in training settings where power relations are amplified. Provided that supervisors respond in a manner conducive to dialogue, embedding opportunities for supervisees to provide feedback about their experience of supervision may increase openness within the relationship. Inviting and responding to feedback involve risk-taking and learning for both parties and can benefit supervisory processes and influence supervisee therapeutic practice. Alongside dialogue, routine outcome/alliance measurement can facilitate feedback processes. However, standardized measures could be problematic to a postmodern practitioner emphasizing individual goals and approaches to learning. We recount our attempt to address this potential tension by co-constructing a measure to tailor supervision towards our mutually agreed supervisee-supervisor goals. The advantages and disadvantages of using this approach are addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".