Silent Thoughts, Spoken Wishes: When Candidate Experience of the Supervisor Converges With Patient Fantasies
Bibliographic record
Abstract
How and why a candidate's private experience of two supervisors emerged in patients' fantasies about them is explored. Four issues are examined in light of two control cases: (1) Patients divide, rather than split, the transference between supervisor and candidate, experiencing both ambivalently. (2) Even a patient with no knowledge of the supervisor's identity may have a fantasy of the supervisor that is congruent with the candidate's experience of the supervisor. (3) When new professional traits emerge in the candidate as he or she identifies with his or her mentor, the patient may attribute them to the invisible person in the room--the supervisor; the patient may intuit and be influenced by the candidate's feelings about the supervisor as well. (4) A patient's fantasies about the supervisor may reflect parallel process in reverse, whereby the patient discerns what is going on between supervisor and candidate through his or her treatment, just as the supervisor reads what is going on between patient and candidate through the candidate's reporting of the treatment. Because the trio is the truth of the training case, it seems fitting and empowering to acknowledge and analyze the role of the supervisor in the patient's mind.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".