Contextualizing the Self: Integrating a Systems Perspective Into Psychoanalytic Therapy
Bibliographic record
Abstract
As a senior therapist, I can rejoice these days in a long career of treating many and various patients in both group and individual therapies. When I first started my private practice back in 1970, I approached my work within a classical analytic framework, revealing little of myself in the therapy relationship, other than my best thoughts and interpretations, and focusing the work on genetic material and the emerging transference. This is what I was taught in graduate school, as most of us were in those days. My fundamental temperament, however, was and is that of an independent or maverick. Being independent, I eschewed the idea of attending an analytic institute after the intensive training years of internship and fellowship. I had had enough supervision, or so I thought, and wanted to spread my wings, to fly solo. My avoidance of formal analytic training has had its downside; I haven’t had a community of like-minded therapists with which to talk and to publish, at least when it comes to my individual work. Also, when I have needed the inevitable supervision or consultation, I have had to seek it outside of ready, institutional affiliations. Al-
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".